Datasets:
License:
| license: apache-2.0 | |
| task_categories: | |
| - reinforcement-learning | |
| - robotics | |
| tags: | |
| - Minari | |
| - TeleArms | |
| - bimanual | |
| - simulation | |
| - offline-rl | |
| # TeleArms offline RL datasets (state) | |
| 30 [Minari](https://minari.farama.org) datasets of human teleoperated demonstrations of 10 bimanual | |
| YAM cube tasks in MuJoCo: each task in three subsets of 1,500 demos, picked at random | |
| (`random-1500`), by player ELO (`elo-1500`) and by demo score (`score-1500`). The ids are | |
| `telearms/<Task>/<subset>-v0`: | |
| ```python | |
| import os | |
| import minari | |
| from huggingface_hub import snapshot_download | |
| root = os.environ.get("MINARI_DATASETS_PATH", os.path.expanduser("~/.minari/datasets")) | |
| snapshot_download("mliu48/telearms", repo_type="dataset", local_dir=os.path.join(root, "telearms"), | |
| allow_patterns="CubePatchPlacement/random-1500-v0/*") | |
| dataset = minari.load_dataset("telearms/CubePatchPlacement/random-1500-v0") | |
| env = dataset.recover_environment() # needs telearms-gym installed | |
| ``` | |
| The environment is `TeleArmsEnv(task, obs_mode="privileged")` in | |
| [telearms-gym](https://github.com/liuwilli10101/telearms-gym) (`gymnasium_env/telearms_env.py`). On a machine without EGL, set `MUJOCO_GL=disable` | |
| before importing it. | |
| **Observations** are the simulator's state, not images: the robot's 14 joint and gripper values, | |
| then each cube's position, orientation and velocity (13 numbers per cube), then each goal patch's | |
| position and orientation (7 per patch). | |
| | Task | Observation size | | |
| |---|---| | |
| | CubePatchPlacement | 54 | | |
| | FrontEdgeCubeRow | 53 | | |
| | FrontToBackCubeLine | 53 | | |
| | LCubeArrangement | 66 | | |
| | LetterFaceAlignment | 27 | | |
| | RedCubeInFrontOfBlueCube | 40 | | |
| | RedLeftOfBluePlacement | 40 | | |
| | SemanticCubeOnRedPatchAUp | 34 | | |
| | SequentialPushBlockToTarget | 54 | | |
| | ThreeCubeWallRow | 53 | | |
| **Rewards.** Every demo was replayed in its task's environment and scored by its rules. `rewards` | |
| is the sparse reward, 1.0 on the step that reaches success, else 0. `infos["reward_dense"]` is the | |
| environment's `reward_type="dense"` reward: 1/N on the first step that reaches each of the task's | |
| N stages (grasp, lift, place, ...) plus 1.0 on success, at most 2 per episode. Each demo ends at | |
| its first success (`terminations`), as the environment ends an episode. Of the 45,000 demos, | |
| 665 (1.5%) were accepted when played but never satisfy the environment's task check; they end with | |
| `truncations` and no reward. | |
| The same demos with camera images are the `offline-rl` branch of | |
| `mliu48/telearms-yam-cube-v5-exp-<subset>`: | |
| `LeRobotDataset("mliu48/telearms-yam-cube-v5-exp-random-1500", revision="offline-rl")`. | |