{
  "schema": "t2/campaign-spec/1",
  "model": "openai-codex/gpt-6-astra",
  "effort": "high",
  "transport": "sse",
  "strict_usage": true,
  "service_tier": "default",
  "seed": 0,
  "mode": "realtime",
  "wall_timeout_s": 3600,
  "max_steps": null,
  "automatic_episode_retries": 0,
  "selected_tasks": 20,
  "maximum_gpu_workers": 8,
  "repo_id": "RLE-Bench/booster-t2-simple-assets-preview",
  "commits": {
    "RoboEnv": "41517b0f36a34631a21bbd8bc26edd0e9d8d20e3",
    "RLE-Bench-inhouse": "ed9f3fbd13c388e2fa496f54f8423363633984f0",
    "kinex": "15207927c6669fe98f1213a3fd05148e0fe938d3"
  },
  "prepared": "[local-path]",
  "cases": [
    {
      "slot": "01-insert-shape-blocks",
      "task": "isaaclab/t2-insert-shape-blocks",
      "instruction": "Pick up the cylinder and the cube and put each through the matching opening in the shape-sorting box. Let go and leave both inside.",
      "task_yaml_sha256": "3e915ffcd15179ddbb900f16295d3923e110b117d6b82dc12c2b3fe6d8561a64",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-insert-shape-blocks\n\nPick up the cylinder and the cube and put each through the matching opening in the shape-sorting box. Let go and leave both inside.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "cube": {
                      "fixed": false,
                      "model": "cube_block",
                      "pos": [
                        0.36885,
                        1.23587,
                        0.762082
                      ],
                      "yaw": 0.0
                    },
                    "cylinder": {
                      "fixed": false,
                      "model": "cylinder_block",
                      "pos": [
                        -0.47115,
                        1.23587,
                        0.762082
                      ],
                      "yaw": 0.0
                    },
                    "sorter": {
                      "fixed": true,
                      "model": "sorter",
                      "pos": [
                        -0.05115,
                        1.48587,
                        0.762082
                      ],
                      "yaw": 0.0
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "cube": "cube",
                        "cylinder": "cylinder",
                        "kind": "insert",
                        "sorter": "sorter"
                      }
                    ]
                  ],
                  "start": [
                    -0.05115,
                    0.21587,
                    1.5707963267948966
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-insert-shape-blocks",
              "instruction": "Pick up the cylinder and the cube and put each through the matching opening in the shape-sorting box. Let go and leave both inside.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "02-ordered-reach",
      "task": "isaaclab/t2-ordered-reach",
      "instruction": "Walk to the table with the blue and red blocks. Touch the blue block with your left hand and move that hand away, then touch the red block with your right hand and move that hand away.",
      "task_yaml_sha256": "393667b637fa1804a283efc6076a09530558ac7f6e1e80e59b9391447b90be69",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-ordered-reach\n\nWalk to the table with the blue and red blocks. Touch the blue block with your left hand and move that hand away, then touch the red block with your right hand and move that hand away.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "blue": {
                      "fixed": true,
                      "model": "blue_block",
                      "pos": [
                        -0.30115,
                        1.22587,
                        0.762082
                      ],
                      "yaw": 0.0
                    },
                    "red": {
                      "fixed": true,
                      "model": "red_block",
                      "pos": [
                        0.19885,
                        1.22587,
                        0.762082
                      ],
                      "yaw": 0.0
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "blue": "blue",
                        "kind": "ordered_touch",
                        "red": "red"
                      }
                    ]
                  ],
                  "start": [
                    -0.05115,
                    0.11587,
                    1.5707963267948966
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-ordered-reach",
              "instruction": "Walk to the table with the blue and red blocks. Touch the blue block with your left hand and move that hand away, then touch the red block with your right hand and move that hand away.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "03-balance-scale",
      "task": "isaaclab/t2-balance-scale",
      "instruction": "Choose weights from the holder and place them in the empty pan until the scale balances. You may add or remove weights as needed. Let go and leave the beam level with the weights resting in the pan.",
      "task_yaml_sha256": "2fbfeec7023875e7b8b1005fb0fb179958db4bae9068a814f2e95305cb0f0b74",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-balance-scale\n\nChoose weights from the holder and place them in the empty pan until the scale balances. You may add or remove weights as needed. Let go and leave the beam level with the weights resting in the pan.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {
                    "scale": {
                      "joints": {
                        "balance": 0.0,
                        "pan_pivot": 0.0,
                        "reference_pivot": 0.0
                      },
                      "model": "balance_scale",
                      "pos": [
                        -0.05115,
                        1.52587,
                        0.762082
                      ],
                      "yaw": 0.0
                    }
                  },
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "holder": {
                      "fixed": true,
                      "model": "tray",
                      "pos": [
                        -0.35115,
                        1.18587,
                        0.762082
                      ],
                      "yaw": 0.0
                    },
                    "weight_1": {
                      "fixed": false,
                      "model": "weight_1",
                      "pos": [
                        -0.47115,
                        1.20587,
                        0.778082
                      ],
                      "yaw": 0.0
                    },
                    "weight_2": {
                      "fixed": false,
                      "model": "weight_2",
                      "pos": [
                        -0.35115,
                        1.20587,
                        0.778082
                      ],
                      "yaw": 0.0
                    },
                    "weight_4": {
                      "fixed": false,
                      "model": "weight_4",
                      "pos": [
                        -0.23115,
                        1.20587,
                        0.778082
                      ],
                      "yaw": 0.0
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "kind": "balance",
                        "scale": "scale",
                        "weights": [
                          "weight_1",
                          "weight_2",
                          "weight_4"
                        ]
                      }
                    ]
                  ],
                  "start": [
                    -0.05115,
                    0.21587,
                    1.5707963267948966
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-balance-scale",
              "instruction": "Choose weights from the holder and place them in the empty pan until the scale balances. You may add or remove weights as needed. Let go and leave the beam level with the weights resting in the pan.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "04-can-in-bowl",
      "task": "isaaclab/t2-can-in-bowl",
      "instruction": "Pick up the tomato soup can and put it inside the bowl. Let go and leave the can resting securely inside, with the bowl standing on the table.",
      "task_yaml_sha256": "411b103cf427c34dc41e7134e6fc5984d9630724e44636214c38f9d0b0c0fe46",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-can-in-bowl\n\nPick up the tomato soup can and put it inside the bowl. Let go and leave the can resting securely inside, with the bowl standing on the table.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "bowl": {
                      "fixed": false,
                      "model": "bowl",
                      "pos": [
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                      "yaw": 0.0
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                    "can": {
                      "fixed": false,
                      "model": "can_tomato",
                      "pos": [
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                        1.24587,
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                      "yaw": 0.0
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "bowl": "bowl",
                        "item": "can",
                        "kind": "contain",
                        "table": "dining_table"
                      }
                    ]
                  ],
                  "start": [
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                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-can-in-bowl",
              "instruction": "Pick up the tomato soup can and put it inside the bowl. Let go and leave the can resting securely inside, with the bowl standing on the table.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "05-hang-mug",
      "task": "isaaclab/t2-hang-mug",
      "instruction": "Pick up the mug and hang it by its handle on the mug rack. Let go and leave it hanging securely.",
      "task_yaml_sha256": "cfc15c2d1c811402831e28f3e13a0298b6df9b924d1f81f1a678305bb186f21f",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-hang-mug\n\nPick up the mug and hang it by its handle on the mug rack. Let go and leave it hanging securely.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "mug": {
                      "fixed": false,
                      "model": "mug",
                      "pos": [
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                      "yaw": 3.141592653589793
                    },
                    "rack": {
                      "fixed": true,
                      "model": "mug_rack",
                      "pos": [
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                      "yaw": 3.141592653589793
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "kind": "hang",
                        "mug": "mug",
                        "rack": "rack"
                      }
                    ]
                  ],
                  "start": [
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                    -0.22335,
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                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-hang-mug",
              "instruction": "Pick up the mug and hang it by its handle on the mug rack. Let go and leave it hanging securely.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "06-scrape-blocks-into-pan",
      "task": "isaaclab/t2-scrape-blocks-into-pan",
      "instruction": "Pick up the scraper and use its blade to push both small wooden blocks along the tabletop into the collection pan. Then put the scraper back on its stand and let go.",
      "task_yaml_sha256": "c1a2e9e6146cbfc8ee4f931f2841a288302ee82fc72de951a0ad1b57d006687f",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-scrape-blocks-into-pan\n\nPick up the scraper and use its blade to push both small wooden blocks along the tabletop into the collection pan. Then put the scraper back on its stand and let go.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "block_a": {
                      "fixed": false,
                      "model": "small_block",
                      "pos": [
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                      "yaw": 0.0
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                    "block_b": {
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                      "model": "small_block",
                      "pos": [
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                      "yaw": 0.0
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                    "pan": {
                      "fixed": true,
                      "model": "collection_pan",
                      "pos": [
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                        1.56587,
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                      "yaw": 0.0
                    },
                    "scraper": {
                      "fixed": false,
                      "model": "scraper",
                      "pos": [
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                      "yaw": 0.0
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                    "stand": {
                      "fixed": true,
                      "model": "tool_stand",
                      "pos": [
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                      "yaw": 0.0
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                  },
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                  "stages": [
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                        "blocks": [
                          "block_a",
                          "block_b"
                        ],
                        "kind": "scrape",
                        "pan": "pan",
                        "stand": "stand",
                        "tool": "scraper"
                      }
                    ]
                  ],
                  "start": [
                    -0.05115,
                    0.21587,
                    1.5707963267948966
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-scrape-blocks-into-pan",
              "instruction": "Pick up the scraper and use its blade to push both small wooden blocks along the tabletop into the collection pan. Then put the scraper back on its stand and let go.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "07-carry-bowl-level",
      "task": "isaaclab/t2-carry-bowl-level",
      "instruction": "Carry the bowl from the kitchen island to the center of the plate on the living-room sideboard, keeping it roughly level as you carry it. Set it down and let go.",
      "task_yaml_sha256": "a1d15ade329219754937510cb93573f1c735acaf3b87fd5a408cfa72a32a6b6a",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-carry-bowl-level\n\nCarry the bowl from the kitchen island to the center of the plate on the living-room sideboard, keeping it roughly level as you carry it. Set it down and let go.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "bowl": {
                      "fixed": false,
                      "model": "bowl",
                      "pos": [
                        0.04989,
                        -1.24335,
                        0.902768
                      ],
                      "yaw": 3.141592653589793
                    },
                    "plate": {
                      "fixed": true,
                      "model": "plate",
                      "pos": [
                        0.9,
                        2.99,
                        0.86377
                      ],
                      "yaw": 0.0
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
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                      {
                        "item": "bowl",
                        "kind": "carry_level",
                        "source": "kitchen_island",
                        "target": "plate"
                      }
                    ]
                  ],
                  "start": [
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                    -0.22335,
                    4.71238898038469
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-carry-bowl-level",
              "instruction": "Carry the bowl from the kitchen island to the center of the plate on the living-room sideboard, keeping it roughly level as you carry it. Set it down and let go.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "08-pull-cart-to-dock",
      "task": "isaaclab/t2-pull-cart-to-dock",
      "instruction": "Take the cart by its handle and pull it around the sofa to the cart bay beside the kitchen island. Park the cart inside the bay with its handle facing the room, then let go and leave it upright and still.",
      "task_yaml_sha256": "302a42e8c7e74afe1006f34d871ec0f8e33a04539ca471628405b89854dd8955",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-pull-cart-to-dock\n\nTake the cart by its handle and pull it around the sofa to the cart bay beside the kitchen island. Park the cart inside the bay with its handle facing the room, then let go and leave it upright and still.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {
                    "cart": {
                      "joints": {
                        "caster_0": 0.0,
                        "caster_1": 0.0,
                        "caster_2": 0.0,
                        "caster_3": 0.0,
                        "wheel_0": 0.0,
                        "wheel_1": 0.0,
                        "wheel_2": 0.0,
                        "wheel_3": 0.0
                      },
                      "model": "cart",
                      "pos": [
                        4.5,
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                      ],
                      "yaw": -1.5707963267948966
                    }
                  },
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {},
                  "scene": "hssd_household",
                  "stages": [
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                      {
                        "cart": "cart",
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              "id": "isaaclab/t2-pull-cart-to-dock",
              "instruction": "Take the cart by its handle and pull it around the sofa to the cart bay beside the kitchen island. Park the cart inside the bay with its handle facing the room, then let go and leave it upright and still.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
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        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "09-deliver-two-objects",
      "task": "isaaclab/t2-deliver-two-objects",
      "instruction": "Carry the apple and the tomato soup can from the kitchen island to the dining table. Place the apple in the center of the round plate and the can in the center of the rectangular tray, then let go of both.",
      "task_yaml_sha256": "03a57529c1fbd5d28af27567e861c77744bbfef447509b9082fcc7fe55e3912d",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-deliver-two-objects\n\nCarry the apple and the tomato soup can from the kitchen island to the dining table. Place the apple in the center of the round plate and the can in the center of the rectangular tray, then let go of both.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "apple": {
                      "fixed": false,
                      "model": "apple",
                      "pos": [
                        0.28989,
                        -1.25335,
                        0.902768
                      ],
                      "yaw": 3.141592653589793
                    },
                    "can": {
                      "fixed": false,
                      "model": "can_tomato",
                      "pos": [
                        -0.19011,
                        -1.25335,
                        0.902768
                      ],
                      "yaw": 3.141592653589793
                    },
                    "plate": {
                      "fixed": true,
                      "model": "plate",
                      "pos": [
                        -0.41115,
                        1.46587,
                        0.762082
                      ],
                      "yaw": 0.0
                    },
                    "tray": {
                      "fixed": true,
                      "model": "tray",
                      "pos": [
                        0.29885,
                        1.46587,
                        0.762082
                      ],
                      "yaw": 0.0
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "items": [
                          "apple",
                          "can"
                        ],
                        "kind": "deliver",
                        "source": "kitchen_island",
                        "targets": [
                          "plate",
                          "tray"
                        ]
                      }
                    ]
                  ],
                  "start": [
                    0.04989,
                    -0.22335,
                    4.71238898038469
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-deliver-two-objects",
              "instruction": "Carry the apple and the tomato soup can from the kitchen island to the dining table. Place the apple in the center of the round plate and the can in the center of the rectangular tray, then let go of both.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "10-slide-can",
      "task": "isaaclab/t2-slide-can",
      "instruction": "Slide the can along the tabletop to the center of the round placemat without picking it up. Let go and leave it resting there.",
      "task_yaml_sha256": "912c65d96c2527d7e0d3c7f0fe5e852cd71ae7d25e8e7e0d2beed9cd9d53b1f4",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-slide-can\n\nSlide the can along the tabletop to the center of the round placemat without picking it up. Let go and leave it resting there.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "can": {
                      "fixed": false,
                      "model": "can_tomato",
                      "pos": [
                        3.81944,
                        -3.29457,
                        0.7493
                      ],
                      "yaw": -1.5707963267948966
                    },
                    "mat": {
                      "fixed": true,
                      "model": "round_mat",
                      "pos": [
                        3.71544,
                        -3.95457,
                        0.7493
                      ],
                      "yaw": -1.5707963267948966
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "item": "can",
                        "kind": "slide",
                        "surface": "reading_desk",
                        "target": "mat"
                      }
                    ]
                  ],
                  "start": [
                    4.50844,
                    -3.71457,
                    -3.141592653589793
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-slide-can",
              "instruction": "Slide the can along the tabletop to the center of the round placemat without picking it up. Let go and leave it resting there.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "11-light-reading-book",
      "task": "isaaclab/t2-light-reading-book",
      "instruction": "Turn on the desk lamp using its switch and adjust the lamp head to light up the open pages of the book. Let go and leave the pages well illuminated.",
      "task_yaml_sha256": "8e60c4c09f94f4c00d37b3ed074d23c4f11a67068c648e078ce3729e214347f1",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-light-reading-book\n\nTurn on the desk lamp using its switch and adjust the lamp head to light up the open pages of the book. Let go and leave the pages well illuminated.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {
                    "lamp": {
                      "joints": {
                        "aim": 1.35,
                        "power": -0.3
                      },
                      "model": "lamp",
                      "pos": [
                        3.52,
                        -3.41457,
                        0.7493
                      ],
                      "yaw": 1.5707963267948966
                    }
                  },
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "book": {
                      "fixed": false,
                      "model": "open_book",
                      "pos": [
                        3.81,
                        -3.41457,
                        0.7493
                      ],
                      "yaw": 1.5707963267948966
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "book": "book",
                        "kind": "illuminate",
                        "lamp": "lamp"
                      }
                    ]
                  ],
                  "start": [
                    4.50844,
                    -3.71457,
                    -3.141592653589793
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-light-reading-book",
              "instruction": "Turn on the desk lamp using its switch and adjust the lamp head to light up the open pages of the book. Let go and leave the pages well illuminated.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "12-rotate-box",
      "task": "isaaclab/t2-rotate-box",
      "instruction": "Keep the cracker box standing on the rectangular placemat and turn it so its front label faces the plate beside the mat. Let go and leave it standing on the mat.",
      "task_yaml_sha256": "1d55d251b3a4f11cfc77d4052fad02ef3cc7bf6c62d80416e797b56ebb5d9504",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-rotate-box\n\nKeep the cracker box standing on the rectangular placemat and turn it so its front label faces the plate beside the mat. Let go and leave it standing on the mat.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "box": {
                      "fixed": false,
                      "model": "cracker_box",
                      "pos": [
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                    },
                    "mat": {
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                      "model": "rectangular_mat",
                      "pos": [
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                    "plate": {
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                      "model": "plate",
                      "pos": [
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                      ],
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                    }
                  },
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                        "item": "box",
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                  ],
                  "version": 5
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              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-rotate-box",
              "instruction": "Keep the cracker box standing on the rectangular placemat and turn it so its front label faces the plate beside the mat. Let go and leave it standing on the mat.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
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        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "13-stack-cans",
      "task": "isaaclab/t2-stack-cans",
      "instruction": "Pick up the tomato soup can and stack it upright on the bean can. Let go and leave the stack standing on the table.",
      "task_yaml_sha256": "6569a0b60fb15652eb58499137a9a3c3fdfa2f0869ba3825f1733fafb91adeb5",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-stack-cans\n\nPick up the tomato soup can and stack it upright on the bean can. Let go and leave the stack standing on the table.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "beans": {
                      "fixed": false,
                      "model": "can_beans",
                      "pos": [
                        3.81294,
                        -3.96457,
                        0.7493
                      ],
                      "yaw": -1.5707963267948966
                    },
                    "tomato": {
                      "fixed": false,
                      "model": "can_tomato",
                      "pos": [
                        3.81294,
                        -3.46457,
                        0.7493
                      ],
                      "yaw": -1.5707963267948966
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "item": "tomato",
                        "kind": "stack",
                        "support": "beans",
                        "table": "reading_desk"
                      }
                    ]
                  ],
                  "start": [
                    4.50844,
                    -3.71457,
                    -3.141592653589793
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-stack-cans",
              "instruction": "Pick up the tomato soup can and stack it upright on the bean can. Let go and leave the stack standing on the table.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "14-open-jar-with-both-hands",
      "task": "isaaclab/t2-open-jar-with-both-hands",
      "instruction": "Hold the storage jar clear of the counter with one hand. While holding it there, use your other hand to remove its lift-off lid. Put the jar back upright on the counter and place the lid on the small dish, then let go of both.",
      "task_yaml_sha256": "10abacc411eff582a4c4154b1e4934d4ec869da7ae5d29782db08f8506482f0d",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-open-jar-with-both-hands\n\nHold the storage jar clear of the counter with one hand. While holding it there, use your other hand to remove its lift-off lid. Put the jar back upright on the counter and place the lid on the small dish, then let go of both.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "dish": {
                      "fixed": true,
                      "model": "dish",
                      "pos": [
                        -0.28011,
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                      "yaw": 3.141592653589793
                    },
                    "jar": {
                      "fixed": false,
                      "model": "jar",
                      "pos": [
                        0.19989,
                        -1.28335,
                        0.902768
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                      "yaw": 3.141592653589793
                    },
                    "lid": {
                      "fixed": false,
                      "model": "jar_lid",
                      "pos": [
                        0.19989,
                        -1.28335,
                        1.052768
                      ],
                      "yaw": 3.141592653589793
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "dish": "dish",
                        "jar": "jar",
                        "kind": "open_jar",
                        "lid": "lid",
                        "table": "kitchen_island"
                      }
                    ]
                  ],
                  "start": [
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                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-open-jar-with-both-hands",
              "instruction": "Hold the storage jar clear of the counter with one hand. While holding it there, use your other hand to remove its lift-off lid. Put the jar back upright on the counter and place the lid on the small dish, then let go of both.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "15-open-trash-lid",
      "task": "isaaclab/t2-open-trash-lid",
      "instruction": "Lift the trash-can lid until the opening is clearly accessible, and briefly keep it open.",
      "task_yaml_sha256": "48b2b2bb1cec261d6a6e1c5eabc6959c0e128c61b1022cbc6fbab471e74d0a71",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-open-trash-lid\n\nLift the trash-can lid until the opening is clearly accessible, and briefly keep it open.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {
                    "bin": {
                      "joints": {
                        "hinge": 0.0
                      },
                      "model": "trash_bin",
                      "pos": [
                        -1.42,
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                        0.0
                      ],
                      "yaw": 3.141592653589793
                    }
                  },
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {},
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "bin": "bin",
                        "kind": "open_lid"
                      }
                    ]
                  ],
                  "start": [
                    -1.42,
                    -0.4,
                    -1.5707963267948966
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-open-trash-lid",
              "instruction": "Lift the trash-can lid until the opening is clearly accessible, and briefly keep it open.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "16-pound-pegs-with-mallet",
      "task": "isaaclab/t2-pound-pegs-with-mallet",
      "instruction": "Pick up the wooden mallet and use its head to tap both raised pegs down until their tops are flush with the pounding bench. Put the mallet back on its stand and let go.",
      "task_yaml_sha256": "d52937a59e1a6f7a52f4e41d4edb2704023dff4214cf488338f04cabc3aaabba",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-pound-pegs-with-mallet\n\nPick up the wooden mallet and use its head to tap both raised pegs down until their tops are flush with the pounding bench. Put the mallet back on its stand and let go.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {
                    "bench": {
                      "joints": {
                        "peg_a": 0.07,
                        "peg_b": 0.07
                      },
                      "model": "pounding_bench",
                      "pos": [
                        0.14885,
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                      "yaw": 0.0
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                  "hold_s": 0.35,
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                    "mallet": {
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                      "model": "mallet",
                      "pos": [
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                    "stand": {
                      "fixed": true,
                      "model": "tool_stand",
                      "pos": [
                        -0.45115,
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                      ],
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                    }
                  },
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                        "bench": "bench",
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                  ],
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              "id": "isaaclab/t2-pound-pegs-with-mallet",
              "instruction": "Pick up the wooden mallet and use its head to tap both raised pegs down until their tops are flush with the pounding bench. Put the mallet back on its stand and let go.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
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        "realtime": true,
        "recording": true,
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          "third_person",
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        ],
        "safety_timeout_sec": 3600,
        "seeds": [
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        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "17-push-chair",
      "task": "isaaclab/t2-push-chair",
      "instruction": "Push the office chair in under the desk and leave it upright and still.",
      "task_yaml_sha256": "b03618ac3457b0e274d0d8dc1c870aa17a393479ad23732521de28feedf56a92",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-push-chair\n\nPush the office chair in under the desk and leave it upright and still.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {
                    "chair": {
                      "joints": {
                        "caster_0": 0.0,
                        "caster_1": 0.0,
                        "caster_2": 0.0,
                        "caster_3": 0.0,
                        "wheel_0": 0.0,
                        "wheel_1": 0.0,
                        "wheel_2": 0.0,
                        "wheel_3": 0.0
                      },
                      "model": "chair",
                      "pos": [
                        4.46,
                        -3.71457,
                        0.004
                      ],
                      "yaw": -1.5707963267948966
                    }
                  },
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {},
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "chair": "chair",
                        "desk": "reading_desk",
                        "kind": "push_chair"
                      }
                    ]
                  ],
                  "start": [
                    5.7,
                    -3.71457,
                    3.141592653589793
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-push-chair",
              "instruction": "Push the office chair in under the desk and leave it upright and still.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "18-fetch-book",
      "task": "isaaclab/t2-fetch-book",
      "instruction": "Go to the bookcase, pick up the book lying on the lower open shelf, and bring it back to the center of the tray on the reading desk. Let go and leave it resting there.",
      "task_yaml_sha256": "1962b10e348e8955b8247bd11c93f445ad4d157faa606e7d9215cf2b5b2306cc",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-fetch-book\n\nGo to the bookcase, pick up the book lying on the lower open shelf, and bring it back to the center of the tray on the reading desk. Let go and leave it resting there.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {},
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "book": {
                      "fixed": false,
                      "model": "book",
                      "pos": [
                        4.8,
                        -1.99,
                        0.65
                      ],
                      "yaw": 3.141592653589793
                    },
                    "tray": {
                      "fixed": true,
                      "model": "tray",
                      "pos": [
                        3.72844,
                        -3.71457,
                        0.7493
                      ],
                      "yaw": -1.5707963267948966
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "item": "book",
                        "kind": "fetch",
                        "source": "book_shelf",
                        "target": "tray"
                      }
                    ]
                  ],
                  "start": [
                    4.47594,
                    -3.71457,
                    -3.141592653589793
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-fetch-book",
              "instruction": "Go to the bookcase, pick up the book lying on the lower open shelf, and bring it back to the center of the tray on the reading desk. Let go and leave it resting there.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "19-store-remote-drawer",
      "task": "isaaclab/t2-store-remote-drawer",
      "instruction": "Pull open the sideboard drawer, put the remote control inside, and close the drawer. Let go and leave the remote stored inside.",
      "task_yaml_sha256": "1c3b986bc2763379770f20a1597717ae85a87df53a2e34e817cecc4940422592",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-store-remote-drawer\n\nPull open the sideboard drawer, put the remote control inside, and close the drawer. Let go and leave the remote stored inside.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {
                    "drawer": {
                      "joints": {
                        "slide": 0.0
                      },
                      "model": "drawer_cabinet",
                      "pos": [
                        -0.8,
                        3.05,
                        0.0
                      ],
                      "yaw": 0.0
                    }
                  },
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "remote": {
                      "fixed": false,
                      "model": "remote",
                      "pos": [
                        -0.55,
                        2.85,
                        0.86
                      ],
                      "yaw": 0.0
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "drawer": "drawer",
                        "kind": "store",
                        "remote": "remote"
                      }
                    ]
                  ],
                  "start": [
                    -0.8,
                    2.4,
                    1.5707963267948966
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-store-remote-drawer",
              "instruction": "Pull open the sideboard drawer, put the remote control inside, and close the drawer. Let go and leave the remote stored inside.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    },
    {
      "slot": "20-deliver-parcel-through-door",
      "task": "isaaclab/t2-deliver-parcel-through-door",
      "instruction": "Push open the door to the entryway. Then pick up the parcel with both hands and carry it with both hands through the doorway to the center of the tray on the entryway console. Set it down and let go.",
      "task_yaml_sha256": "95e092b7206efdb76b15240e6d483d38c789b210a28df6564fb65078163cd021",
      "protocol": {
        "episode_specs": [
          {
            "instruction_template": {
              "bindings": {},
              "policy": "original_native",
              "prompt": "<!-- rlebench:resolved-episode-instruction -->\n# Task: isaaclab/t2-deliver-parcel-through-door\n\nPush open the door to the entryway. Then pick up the parcel with both hands and carry it with both hands through the doorway to the center of the tray on the entryway console. Set it down and let go.\n\n## Work to do\n\nSolve the task efficiently. The simulation is already running.\nFollow the task instruction above, including its completion requirements.\nUse `roboenv describe --endpoint \"$ROBOENV_ENDPOINT\"` to inspect the environment\nand public task context. Its native instruction is reference text; the task\ninstruction above already includes this episode's required assignments.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have no limit on native action frames. Execution mode is `realtime`: physics continues during reasoning and tool calls. The world's `wait` action waits for the already-running simulation; it does not grant additional time or pause physics. Buffered motion stops at its declared duration.\nJoint and gripper writes use `actions` and `duration_s` to submit a buffer and\nreturn immediately. Concurrent waits share the same clock.\nReads and writes do not advance physics by themselves. Submission or\nelapsed time does not prove arrival; inspect measured feedback. Device stop\naffects only its declared scope without ending the episode. The safety timeout is\n3600 seconds, including updates to saved resources.\nDo not launch or reset a simulation.\n\n\n\n## Completion\n\nUse `roboenv status --endpoint \"$ROBOENV_ENDPOINT\" --json` to check the simulation\nstate and remaining budget. It does not report success or failure. When finished,\ncall `roboenv stop --endpoint \"$ROBOENV_ENDPOINT\" --json` to finalize the attempt\nand receive the final judgment. Stopping does not grant a retry. If the simulation\nhas already terminated, use the same stop command to retrieve its final judgment.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nThe robot is **Booster T2 with BrainCo Revo2 hands**, running in **isaaclab**.\n\n| Device | Type |\n|---|---|\n| `base` | velocity |\n| `head` | joint |\n| `head_camera` | rgbd |\n| `left_arm` | joint |\n| `left_hand` | dexterous_hand |\n| `right_arm` | joint |\n| `right_hand` | dexterous_hand |\n| `world` | world |\n\nWork in `/workspace`. The RoboEnv Python SDK and CLI are installed, and\n`ROBOENV_ENDPOINT` identifies the running service. Read `harness.md` before issuing\ncommands: RoboEnv supplies the connection procedure, device APIs, each vector\ndimension, units, limits, frames, geometry, and sensor formats there.\n\n```python\nimport os\nimport roboenv\nenv = roboenv.connect(os.environ['ROBOENV_ENDPOINT'])\nprint(env.devices())\nprint(env.status())\n```\n\nUse `env.device(name).read()` and the documented write arguments. Infer scene\ngeometry from camera observations. An accepted command is not proof of arrival;\ninspect measured feedback. Update reusable tools, skills, and memos in place.\n\n"
            },
            "max_steps": null,
            "provider": "roboenv",
            "seed": 0,
            "task": {
              "backend_kwargs": {
                "max_steps": null,
                "task_name": "t2-household",
                "task_spec": {
                  "articulated": {
                    "door": {
                      "joints": {
                        "hinge": 0.0
                      },
                      "model": "door",
                      "pos": [
                        2.2,
                        -4.85,
                        0.01
                      ],
                      "yaw": -1.5707963267948966
                    }
                  },
                  "boxes": [],
                  "hold_s": 0.35,
                  "items": {
                    "parcel": {
                      "fixed": false,
                      "model": "parcel",
                      "pos": [
                        1.65,
                        -0.05,
                        0.82547
                      ],
                      "yaw": 1.5707963267948966
                    },
                    "tray": {
                      "fixed": true,
                      "model": "tray",
                      "pos": [
                        0.5,
                        -5.95,
                        0.82547
                      ],
                      "yaw": 0.0
                    }
                  },
                  "scene": "hssd_household",
                  "stages": [
                    [
                      {
                        "door": "door",
                        "kind": "parcel",
                        "parcel": "parcel",
                        "source": "parcel_console",
                        "target": "tray"
                      }
                    ]
                  ],
                  "start": [
                    2.35,
                    -0.05,
                    3.141592653589793
                  ],
                  "version": 5
                }
              },
              "budget_mode": "practice",
              "execution_mode": "realtime",
              "id": "isaaclab/t2-deliver-parcel-through-door",
              "instruction": "Push open the door to the entryway. Then pick up the parcel with both hands and carry it with both hands through the doorway to the center of the tray on the entryway console. Set it down and let go.",
              "robot": "isaaclab/isaaclab-booster-t2",
              "seed": 0,
              "stop_on_success": true
            },
            "yaml": "/opt/benchmark/episode_01.yaml"
          }
        ],
        "episodes": 1,
        "instruction_policy": "original_native",
        "max_steps": null,
        "memory_retention": "retain",
        "previous_episode_feedback": false,
        "provider": "roboenv",
        "realtime": true,
        "recording": true,
        "required_recording_views": [
          "third_person",
          "head",
          "hand"
        ],
        "safety_timeout_sec": 3600,
        "seeds": [
          0
        ],
        "step_unit": "native action frames",
        "version": "episodic-learning-v1"
      }
    }
  ],
  "physics_fixed_tasks": [
    "05-hang-mug",
    "07-carry-bowl-level",
    "09-deliver-two-objects",
    "14-open-jar-with-both-hands",
    "17-push-chair"
  ],
  "physics_fixed_maximum_gpu_workers": 3
}