full.diff

diff --git a/.gitignore b/.gitignore
index da35b01..488cf45 100755
--- a/.gitignore
+++ b/.gitignore
@@ -217,3 +217,6 @@ eval_result/
 
 **/results/
 .codex
+
+
+personal_materials/
\ No newline at end of file
diff --git a/policy/pi05/deploy_policy.py b/policy/pi05/deploy_policy.py
index 895256f..937a65a 100644
--- a/policy/pi05/deploy_policy.py
+++ b/policy/pi05/deploy_policy.py
@@ -36,6 +36,47 @@ def _format_env_action(action, env):
     return action_tensor.unsqueeze(0)
 
 
+def _as_done(value):
+    """truncated/terminated 는 환경 래퍼에 따라 bool, np.ndarray, torch.Tensor 로 온다.
+    eval_policy.RecordingEnvProxy._as_done 과 같은 방식으로 관용적으로 처리한다."""
+    if isinstance(value, torch.Tensor):
+        return bool(value.any().item())
+    if isinstance(value, np.ndarray):
+        return bool(value.any())
+    return bool(value)
+
+
+
+def _arm(state):
+    """Joint values without the two grippers, which use a different unit."""
+    a = np.asarray(state, dtype=np.float32).reshape(-1)
+    return np.concatenate([a[0:6], a[7:13]])
+
+
+def _stuck_step(model, state):
+    """Track how little the arm has moved between inferences.
+
+    The arm barely moving while the episode keeps running is what a failed
+    click_bell rollout looks like: the policy re-presses the same spot. Both
+    thresholds were read off four recorded episodes and are not settled.
+    """
+    prev = getattr(model, "_prev_arm", None)
+    cur = _arm(state)
+    model._prev_arm = cur
+    if prev is None:
+        return 0
+    moved = float(np.linalg.norm(cur - prev))
+    model._stuck = (getattr(model, "_stuck", 0) + 1) if moved < model.stuck_thresh else 0
+    # Split by arm as well: a combined norm hides the case where one arm is
+    # parked and the other flails, which is what the click_bell rollouts look like.
+    left = float(np.linalg.norm(cur[0:6] - prev[0:6]))
+    right = float(np.linalg.norm(cur[6:12] - prev[6:12]))
+    # One line per inference: enough to rebuild the curve afterwards, without the
+    # 1.2 MB per call that PolicyRecorder would write.
+    print(f"  [move] {moved:.4f} L {left:.4f} R {right:.4f}", flush=True)
+    return model._stuck
+
+
 def encode_obs(obs):
     """Convert gym Gymnasium Dict observation to π₀ input format.
 
@@ -78,6 +119,11 @@ def get_model(usr_args):
     checkpoint_id = int(usr_args.get("checkpoint_id", 30000))
     pi0_step = int(usr_args.get("pi0_step", 50))
     pytorch_device = usr_args.get("pytorch_device", "cuda")
+    record_dir = usr_args.get("record_dir") or None
+    stuck_prompt = usr_args.get("stuck_prompt") or None
+    instruction_override = usr_args.get("instruction_override") or None
+    stuck_thresh = float(usr_args.get("stuck_thresh", 0.08))
+    stuck_after = int(usr_args.get("stuck_after", 3))
 
     if train_config_name is None or model_name is None:
         raise ValueError(
@@ -90,7 +136,12 @@ def get_model(usr_args):
         checkpoint_id=checkpoint_id,
         pi0_step=pi0_step,
         pytorch_device=pytorch_device,
+        record_dir=record_dir,
     )
+    model.stuck_prompt = stuck_prompt
+    model.instruction_override = instruction_override
+    model.stuck_thresh = stuck_thresh
+    model.stuck_after = stuck_after
     return model
 
 
@@ -105,12 +156,24 @@ def eval(env, model, obs):
     """
     # Set language instruction if first call
     if model.observation_window is None:
-        instruction = getattr(env, "_current_instruction", None)
+        instruction = (getattr(model, "instruction_override", None)
+                       or getattr(env, "_current_instruction", None))
         model.set_language(instruction)
 
     inference_times_s = []
     started_at = start_inference(model.pytorch_device)
     img_arr, state = encode_obs(obs)
+
+    # When the arm stops moving, swap in a different instruction and see whether
+    # the policy leaves the state it is caught in. Nothing else changes.
+    # Always measure -- the log line is the only record of the trajectory, and it
+    # is needed on control runs that carry no stuck_prompt at all.
+    n = _stuck_step(model, state)
+    if getattr(model, "stuck_prompt", None):
+        if n >= model.stuck_after and not getattr(model, "_swapped", False):
+            print(f"  [stuck] {n} idle inferences -> prompt swapped", flush=True)
+            model.set_language(model.stuck_prompt)
+            model._swapped = True
     model.update_observation_window(img_arr, state)
     actions = model.get_action()[: model.pi0_step]
     action_tensors = [_format_env_action(action, env) for action in actions]
@@ -127,7 +190,7 @@ def eval(env, model, obs):
 
         if env.get_wrapper_attr("is_task_success")():
             break
-        if truncated.any():
+        if _as_done(truncated):
             break
 
         # Update observation window after each step
@@ -138,5 +201,8 @@ def eval(env, model, obs):
 
 
 def reset_model(model):
+    model._prev_arm = None
+    model._stuck = 0
+    model._swapped = False
     """Reset π₀ internal state (observation window and instruction)."""
     model.reset_obsrvationwindows()
diff --git a/policy/pi05/deploy_policy.yml b/policy/pi05/deploy_policy.yml
index ca85751..64c7085 100755
--- a/policy/pi05/deploy_policy.yml
+++ b/policy/pi05/deploy_policy.yml
@@ -26,4 +26,7 @@ eval_expert_check: true
 # ------------------------------------------------------------------
 train_config_name: null
 checkpoint_id: 30000
+# Set to a directory path to dump raw inference inputs/outputs as .npy per call.
+# Leave null for normal runs: recording writes one file per policy call and is slow.
+record_dir: null
 pi0_step: 10 # It only represents the number of execution steps. not the horizon of reasoning, it's the horizon of execution.
diff --git a/policy/pi05/pi_model.py b/policy/pi05/pi_model.py
index 53aaf63..9f19050 100755
--- a/policy/pi05/pi_model.py
+++ b/policy/pi05/pi_model.py
@@ -26,25 +26,33 @@ import os
 
 class PI0:
 
-    def __init__(self, train_config_name, model_name, checkpoint_id, pi0_step):
+    def __init__(self, train_config_name, model_name, checkpoint_id, pi0_step,
+                 pytorch_device="cuda", record_dir=None):
         self.train_config_name = train_config_name
         self.model_name = model_name
         self.checkpoint_id = checkpoint_id
 
-        specified_path = f"policy/pi05/checkpoints/{self.train_config_name}/{self.model_name}/{self.checkpoint_id}/assets/"
-        entries = os.listdir(specified_path)
-        assets_id = entries[0]
-
         config = _config.get_config(self.train_config_name)
+        # norm stats are resolved from the checkpoint's assets/<asset_id>/ by
+        # create_trained_policy itself (asset_id defaults to the training repo_id).
+        # The old `robotwin_repo_id=` kwarg is a RoboTwin leftover and no longer exists.
         self.policy = _policy_config.create_trained_policy(
             config,
             f"policy/pi05/checkpoints/{self.train_config_name}/{self.model_name}/{self.checkpoint_id}",
-            robotwin_repo_id=assets_id,
             )
+        if record_dir:
+            # Dump every inference's inputs and outputs for offline inspection.
+            import os as _os
+            from openpi.policies.policy import PolicyRecorder
+            _os.makedirs(record_dir, exist_ok=True)
+            self.policy = PolicyRecorder(self.policy, record_dir)
         print("loading model success!")
         self.img_size = (224, 224)
         self.observation_window = None
         self.pi0_step = pi0_step
+        # deploy_policy.eval() reads this for start/finish_inference timing
+        # (torch.cuda.synchronize when it starts with "cuda"). Inference itself is JAX.
+        self.pytorch_device = pytorch_device
 
     # set img_size
     def set_img_size(self, img_size):
@@ -67,13 +75,17 @@ class PI0:
         img_right = np.transpose(img_right, (2, 0, 1))
         img_left = np.transpose(img_left, (2, 0, 1))
 
+        # Keys must match what EmbodiChainInputs (openpi/policies/libero_policy.py)
+        # reads. During training a RepackTransform produced these from the raw
+        # dataset columns (cam_high.color -> observation/image, observation.qpos ->
+        # observation/state, ...); create_trained_policy applies no repack at
+        # inference time, so we have to emit the post-repack names directly.
+        # The old {"state", "images": {"cam_high": ...}} shape is a RoboTwin leftover.
         self.observation_window = {
-            "state": state,
-            "images": {
-                "cam_high": img_front,
-                "cam_left_wrist": img_left,
-                "cam_right_wrist": img_right,
-            },
+            "observation/image": img_front,
+            "observation/left_wrist_image": img_left,
+            "observation/right_wrist_image": img_right,
+            "observation/state": state,
             "prompt": self.instruction,
         }
 
diff --git a/policy/pi05/pyproject.toml b/policy/pi05/pyproject.toml
index 4124b1f..65ea178 100755
--- a/policy/pi05/pyproject.toml
+++ b/policy/pi05/pyproject.toml
@@ -37,7 +37,7 @@ dependencies = [
     "transformers==4.53.2",
     "rich>=14.0.0",
     "polars>=1.30.0",
-    "dexsim-engine==0.4.1",
+    "dexsim-engine==0.4.3",
     "embodichain",
     "robosynchallenge",
 ]
diff --git a/policy/pi05/src/openpi/models/gemma.py b/policy/pi05/src/openpi/models/gemma.py
index d1623d1..8921758 100755
--- a/policy/pi05/src/openpi/models/gemma.py
+++ b/policy/pi05/src/openpi/models/gemma.py
@@ -25,6 +25,7 @@ We follow this einsum axis naming convention:
   D: d_model ("features")
 """
 
+import contextlib
 from collections.abc import Sequence
 import dataclasses
 from typing import Literal, TypeAlias
@@ -154,6 +155,23 @@ class Embedder(nn.Module):
         return jnp.dot(x, self.input_embedding_table.T)
 
 
+# Set to True (via ``capture_attention()``) to have ``Attention`` sow its
+# softmax output into the "intermediates" collection. Analysis only.
+CAPTURE_ATTENTION = False
+
+
+@contextlib.contextmanager
+def capture_attention():
+    """Temporarily enable attention capture inside ``Attention.__call__``."""
+    global CAPTURE_ATTENTION
+    previous = CAPTURE_ATTENTION
+    CAPTURE_ATTENTION = True
+    try:
+        yield
+    finally:
+        CAPTURE_ATTENTION = previous
+
+
 @at.typecheck
 class Attention(nn.Module):
     """Attention module."""
@@ -227,6 +245,13 @@ class Attention(nn.Module):
 
         probs = jax.nn.softmax(masked_logits, axis=-1).astype(dtype)
 
+        # Analysis hook: expose the attention matrix so callers can inspect which
+        # prefix tokens (image patches / language) a query token attends to.
+        # Off by default -- keeping (B, K, G, T, S) for every layer costs GBs and
+        # slows training down, so this must never be on during a training run.
+        if CAPTURE_ATTENTION:
+            self.sow("intermediates", "attn_probs", probs)
+
         encoded = jnp.einsum("BKGTS,BSKH->BTKGH", probs, v)
         encoded = einops.rearrange(encoded, "B T K G H -> B T (K G) H")
 
@@ -356,15 +381,23 @@ class Module(nn.Module):
             embed_dim=self.configs[0].width,  # embedder for first expert only
             name="embedder",
         )
-        block_cls = nn.remat(
-            Block,
-            prevent_cse=False,
-            static_argnums=(5,),  # 0=self, 6=deterministic
-            policy=jax.checkpoint_policies.nothing_saveable,
-        )
+        # Attention capture needs the sown values to survive the layer stack:
+        # ``nn.remat`` discards them on recompute, and ``nn.scan`` only carries
+        # collections named in ``variable_axes``. Both detours are analysis-only.
+        if CAPTURE_ATTENTION:
+            block_cls = Block
+            scan_variable_axes = {"params": 0, "intermediates": 0}
+        else:
+            block_cls = nn.remat(
+                Block,
+                prevent_cse=False,
+                static_argnums=(5,),  # 0=self, 6=deterministic
+                policy=jax.checkpoint_policies.nothing_saveable,
+            )
+            scan_variable_axes = {"params": 0}
         self.layers = nn.scan(
             block_cls,
-            variable_axes={"params": 0},
+            variable_axes=scan_variable_axes,
             split_rngs={"params": True, "dropout": True},
             in_axes=(
                 0,
@@ -385,6 +418,14 @@ class Module(nn.Module):
     def embed(self, tokens: at.Int[at.Array, "b t"]) -> at.Float[at.Array, "b t d"]:
         return self.embedder.encode(tokens).astype(self.embed_dtype)
 
+    def decode(self, x):
+        """Vocabulary logits for hidden states, via the tied input embeddings.
+
+        The weights carry a language head even though the flow-matching path never
+        uses it; this exposes it so the checkpoint's text ability can be inspected.
+        """
+        return self.embedder.decode(x)
+
     @at.typecheck
     def __call__(
         self,
diff --git a/policy/pi05/src/openpi/training/config.py b/policy/pi05/src/openpi/training/config.py
index af4ec51..89b23da 100755
--- a/policy/pi05/src/openpi/training/config.py
+++ b/policy/pi05/src/openpi/training/config.py
@@ -852,7 +852,7 @@ _CONFIGS = [
         name="pi05_base_robosynchallenge_full",
         model=pi0_config.Pi0Config(pi05=True,action_horizon=50),
         data=LeRobotEmbodiChainDataConfig(
-            repo_id="your_repo_id",
+            repo_id="RoboSynChallenge/cobotmagic_Sim_click_bell",
             # Replace with your repo-id
             base_config=DataConfig(prompt_from_task=True),
             # EmbodiChain datasets are usually absolute-action targets.
@@ -865,6 +865,54 @@ _CONFIGS = [
         batch_size=64,
         fsdp_devices=1,
     ),
+    # --- added: LoRA low-memory config for RoboSynChallenge (fits 3 x RTX 3090) ---
+    TrainConfig(
+        name="pi05_robosyn_click_bell_lora",
+        model=pi0_config.Pi0Config(
+            pi05=True,
+            action_horizon=50,
+            paligemma_variant="gemma_2b_lora",
+            action_expert_variant="gemma_300m_lora",
+        ),
+        data=LeRobotEmbodiChainDataConfig(
+            repo_id="RoboSynChallenge/cobotmagic_Sim_click_bell",
+            base_config=DataConfig(prompt_from_task=True),
+            extra_delta_transform=True,
+        ),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"),
+        freeze_filter=pi0_config.Pi0Config(
+            pi05=True,
+            action_horizon=50,
+            paligemma_variant="gemma_2b_lora",
+            action_expert_variant="gemma_300m_lora",
+        ).get_freeze_filter(),
+        ema_decay=None,          # LoRA: EMA off
+        num_train_steps=20_000,  # full run: ~1.7 s/it on one 3090 => ~9.5 h
+        # Single GPU on purpose: these 3090s have no NVLink, so FSDP across them
+        # spent ~40s/step just all-gathering the 12.4GB params over PCIe (~3.5GB/s,
+        # sm 100% but mem-util 2% = waiting, not computing). One device = no comms.
+        batch_size=4,
+        fsdp_devices=1,
+        # Shared account: set WANDB_API_KEY and WANDB_ENTITY=robosyn333 in the
+        # environment. --exp-name becomes the run name, so put the varied setting
+        # in it (e.g. jisue_click_bell_lr5e-5_20k).
+        wandb_enabled=True,
+        project_name="robosync",  # default "openpi" would mix with other projects
+        # Not enabled yet -- decide before the next run. Leaving lr_schedule unset
+        # picks up the default CosineDecaySchedule (peak 2.5e-5 decaying to 8.5e-6
+        # by step 20k), which is NOT what openpi's own fine-tune configs do: they
+        # set peak_lr == decay_lr == 5e-5 with decay_steps=1_000_000, i.e. a
+        # constant 5e-5 after warmup. See pi05_libero / pi0_fast_full_droid_finetune.
+        # lr_schedule=_optimizer.CosineDecaySchedule(
+        #     warmup_steps=1_000, peak_lr=5e-5,
+        #     decay_steps=1_000_000, decay_lr=5e-5,
+        # ),
+        save_interval=5_000,
+        # Pin every saved step so the mid-training checkpoints survive long enough to
+        # be evaluated (max_to_keep=1 would delete each one as the next is written).
+        # 4 kept checkpoints x ~9 GB = ~36 GB; older ones get removed once evaluated.
+        keep_period=5_000,
+    ),
     TrainConfig(
         name="pi05_libero",
         model=pi0_config.Pi0Config(pi05=True, action_horizon=10, discrete_state_input=False),
diff --git a/policy/pi05/uv.lock b/policy/pi05/uv.lock
index 3af1706..21f513e 100755
--- a/policy/pi05/uv.lock
+++ b/policy/pi05/uv.lock
@@ -15,8 +15,10 @@ members = [
 ]
 overrides = [
     { name = "av", specifier = "==15.1.0" },
+    { name = "dexsim-engine", specifier = "==0.4.3", index = "http://pyp.open3dv.site:2345/simple/" },
     { name = "lerobot", git = "https://github.com/huggingface/lerobot?rev=0cf864870cf29f4738d3ade893e6fd13fbd7cdb5" },
     { name = "ml-dtypes", specifier = "==0.4.1" },
+    { name = "pymeshlab", specifier = "==2023.12.post3" },
     { name = "tensorboard", specifier = ">=2.15,<2.16" },
     { name = "tensorstore", specifier = "==0.1.74" },
 ]
@@ -152,8 +154,8 @@ name = "array-record"
 version = "0.7.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "absl-py", marker = "sys_platform != 'darwin' and sys_platform != 'emscripten'" },
-    { name = "etils", extra = ["epath"], marker = "sys_platform != 'darwin' and sys_platform != 'emscripten'" },
+    { name = "absl-py" },
+    { name = "etils", extra = ["epath"] },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/da/9e/df7e365bb7516b90709964bd7ca851ad03276a3b33331939bed5cb6d9377/array_record-0.7.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9426431053276e61c9c952393ff37c80825b4edc2fde32aee18b8dc0d653f19c", size = 2135376, upload-time = "2025-04-29T15:26:34.445Z" },
@@ -842,7 +844,7 @@ wheels = [
 
 [[package]]
 name = "dexsim-engine"
-version = "0.4.1"
+version = "0.4.3"
 source = { registry = "http://pyp.open3dv.site:2345/simple/" }
 dependencies = [
     { name = "coacd" },
@@ -868,14 +870,13 @@ dependencies = [
     { name = "scipy" },
     { name = "setuptools" },
     { name = "shapely" },
-    { name = "tetgen" },
     { name = "trimesh" },
     { name = "types-usd" },
     { name = "usd-core" },
     { name = "warp-lang" },
 ]
 wheels = [
-    { url = "http://pyp.open3dv.site:2345/packages/dexsim_engine-0.4.1-cp311-cp311-manylinux_2_31_x86_64.whl", hash = "sha256:7813ba570f9d3ea5d21c8165dc53e0941fea57035f087139d1a34f5f80f8359b" },
+    { url = "http://pyp.open3dv.site:2345/packages/dexsim_engine-0.4.3-cp311-cp311-manylinux_2_31_x86_64.whl", hash = "sha256:9c10f2ff78f6de36cbf2cd9c20d4d04ae900d3755b365207b4829b2fcc4432e3" },
 ]
 
 [[package]]
@@ -1088,14 +1089,16 @@ requires-dist = [
     { name = "black", specifier = "==26.3.1" },
     { name = "bpy", marker = "extra == 'gensim'", index = "https://download.blender.org/pypi/" },
     { name = "casadi" },
-    { name = "dexsim-engine", specifier = "==0.4.1" },
+    { name = "dexsim-engine", specifier = "==0.4.3" },
     { name = "fvcore" },
     { name = "gymnasium", specifier = ">=0.29.1" },
     { name = "h5py" },
     { name = "langchain" },
     { name = "langchain-openai" },
     { name = "lerobot", specifier = ">=0.4.4" },
+    { name = "matplotlib", marker = "extra == 'gensim'" },
     { name = "ortools" },
+    { name = "pillow", marker = "extra == 'gensim'" },
     { name = "pin" },
     { name = "pin-pink" },
     { name = "polars", specifier = "==1.31.0" },
@@ -1104,6 +1107,8 @@ requires-dist = [
     { name = "pytorch-kinematics", specifier = "==0.10.0" },
     { name = "pyyaml", specifier = ">=6.0" },
     { name = "qpsolvers", extras = ["osqp"], specifier = "==4.8.1" },
+    { name = "requests", marker = "extra == 'gensim'" },
+    { name = "scipy", marker = "extra == 'gensim'" },
     { name = "setuptools", specifier = ">=78.1.1" },
     { name = "tensorboard", specifier = ">=2.20.0" },
     { name = "tensordict" },
@@ -2737,7 +2742,7 @@ wheels = [
 
 [[package]]
 name = "mujoco"
-version = "3.6.0"
+version = "3.8.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "absl-py" },
@@ -2746,18 +2751,18 @@ dependencies = [
     { name = "numpy" },
     { name = "pyopengl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/42/82/f8f08dfe9123df4351b560f894f0e7166c1a45a0dd2f04145ed00b8f849b/mujoco-3.6.0.tar.gz", hash = "sha256:15c89f423e33bce0860ad7061763b72323426d6348d7b2e46ebdcc37b11e0905", size = 915041, upload-time = "2026-03-11T01:45:42.807Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ff/ba/ad135f7a4a71360072bc4f202f7ab3130d7d7827cff70ce3e1b382a1a410/mujoco-3.8.1.tar.gz", hash = "sha256:019a0b3406892bc98454eaf55cbd27f85d167758ad785f77c608a61f3a34ad17", size = 922269, upload-time = "2026-05-11T13:44:01.357Z" }
 wheels = [
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 [[package]]
 name = "threadpoolctl"
 version = "3.6.0"
@@ -5476,7 +5459,7 @@ name = "triton"
 version = "3.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "setuptools", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" },
+    { name = "setuptools" },
 ]
 wheels = [
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@@ -5617,8 +5600,8 @@ name = "uvicorn"
 version = "0.49.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "click", marker = "sys_platform != 'emscripten'" },
-    { name = "h11", marker = "sys_platform != 'emscripten'" },
+    { name = "click" },
+    { name = "h11" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c4/1f/fa18009dea8469069cca78a4e877a008ab78f08b064bfc9ab891579077ff/uvicorn-0.49.0.tar.gz", hash = "sha256:ebf4271aa580d9de97f93192d4595176df6e91f9aae919ca73e4fc07df1e66a3", size = 91284, upload-time = "2026-06-03T22:01:30.448Z" }
 wheels = [
@@ -5735,16 +5718,16 @@ wheels = [
 
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-version = "1.14.0"
+version = "1.13.0"
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+    { url = "https://files.pythonhosted.org/packages/1e/79/e4117177b88df67d7967c993a2234a3888d0e388076652e1f2a50551f95a/warp_lang-1.13.0-py3-none-win_amd64.whl", hash = "sha256:47975ea07252d45a4d09d2d1a6cffc55002fa7fbde771c8dceb1baf4b32d4fb8", size = 121601324, upload-time = "2026-05-04T04:31:37.814Z" },
 ]
 
 [[package]]
diff --git a/scripts/eval_policy.py b/scripts/eval_policy.py
index f835f4f..2237077 100755
--- a/scripts/eval_policy.py
+++ b/scripts/eval_policy.py
@@ -862,7 +862,12 @@ def main():
     finally:
         if video_recorder:
             video_recorder.close_episode(success=False)
-        env.close()
+        # NOTE: env.close() is deliberately NOT called here.
+        # EmbodiChain's sim_manager.destroy() ends with os._exit(0), which kills the
+        # process on the spot. Calling it in this finally block meant that (a) the
+        # metrics json and the summary below were never written, and (b) any exception
+        # raised during a rollout was destroyed before Python could print its traceback.
+        # The close now happens at the very end of main(), after results are saved.
 
     inference_call_count = len(all_inference_times)
     average_action_steps = float(np.mean(action_steps))
@@ -951,6 +956,9 @@ def main():
     print(f"  Metrics saved to: {result_path}")
     print(f"{'='*50}")
 
+    # Results are on disk now, so it is safe to let the simulator hard-exit the process.
+    env.close()
+
 
 if __name__ == "__main__":
     main()