refactor(g05): defer PandaOmron embodiment mapping

docs/source/g05.mdx

@@ -60,10 +60,7 @@ The converted checkpoints are private under the LeRobot organization:
 
 `lerobot/g05_base` supplies those 27D model weights, both action heads, the
 ActionCodec tokenizer, and the released six-step R1 Lite processor/statistics
-contract. OpenGalaxea's base release does not contain an `atomic_4` processor or
+contract.
-Atomic-4 dataset statistics. Loading the base weights for Atomic-4 therefore
-requires an `atomic_4` `G05Config` plus statistics computed from the target
-Atomic-4 dataset; reusing the R1 Lite statistics would be incorrect.
 
 ## Installation Requirements
 

src/lerobot/policies/g05/configuration_g05.py

@@ -51,11 +51,6 @@ G05_CAMERA_PROFILES: dict[str, tuple[str, ...]] = {
         "observation.images.left_wrist_rgb",
         "observation.images.right_wrist_rgb",
     ),
-    "atomic_4": (
-        "observation.images.robot0_agentview_left",
-        "observation.images.robot0_eye_in_hand",
-        "observation.images.robot0_agentview_right",
-    ),
 }
 
 G05_CAMERA_SIZE_PROFILES: dict[str, dict[str, tuple[int, int]]] = {
@@ -64,7 +59,6 @@ G05_CAMERA_SIZE_PROFILES: dict[str, dict[str, tuple[int, int]]] = {
     "so100": dict.fromkeys(G05_CAMERA_PROFILES["so100"], (256, 256)),
     "galaxea_r1lite": dict.fromkeys(G05_CAMERA_PROFILES["galaxea_r1lite"], (256, 256)),
     "galaxea_r1pro": dict.fromkeys(G05_CAMERA_PROFILES["galaxea_r1pro"], (256, 256)),
-    "atomic_4": dict.fromkeys(G05_CAMERA_PROFILES["atomic_4"], (256, 256)),
 }
 
 
@@ -126,8 +120,7 @@ def make_g05_cot_prompt_template(
 
 # Raw dimensions are inserted in these exact policy slots. The G0.5 shared layout is:
 # left_control[9] | left_gripper[1] | right_control[9] | right_gripper[1] | lower_body[7].
-# LIBERO uses only the right EEF delta and right gripper. atomic_4 is a single-arm mobile
+# LIBERO uses only the right EEF delta and right gripper.
-# manipulator and therefore has a deliberately separate state/action map.
 G05_EMBODIMENT_MAPPINGS: dict[str, dict[str, tuple[int, ...]]] = {
     "libero": {
         "state": (10, 11, 12, 13, 14, 15, 19),
@@ -149,14 +142,6 @@ G05_EMBODIMENT_MAPPINGS: dict[str, dict[str, tuple[int, ...]]] = {
         "state": (0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 19),
         "action": (0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 19),
     },
-    "atomic_4": {
-        # EEF relative xyz+quat -> right_control[0:7], base xyz+quat -> lower_body[0:7],
-        # the two parallel-jaw qpos values -> the two one-dimensional gripper slots.
-        "state": (10, 11, 12, 13, 14, 15, 16, 20, 21, 22, 23, 24, 25, 26, 9, 19),
-        # EEF delta xyz+rpy -> right_control[0:6], gripper -> right_gripper,
-        # base motion[4] -> lower_body[0:4], control mode -> lower_body[4].
-        "action": (10, 11, 12, 13, 14, 15, 19, 20, 21, 22, 23, 24),
-    },
 }
 
 G05_POLICY_PARTS: dict[int, dict[str, int]] = {
@@ -307,14 +292,6 @@ class G05Config(PreTrainedConfig):
             raise ValueError("At least one G0.5 action path must be enabled.")
         if self.embodiment not in G05_EMBODIMENT_MAPPINGS:
             raise ValueError(f"No named G0.5 embodiment mapping for {self.embodiment!r}.")
-        if self.embodiment == "atomic_4":
-            if self.policy_action_dim < 27 or self.policy_state_dim < 27:
-                raise ValueError(
-                    "atomic_4 includes mobile-base/control-mode semantics and requires the 27D "
-                    "G0.5 shared layout; a 20D LIBERO checkpoint is incompatible."
-                )
-            if self.raw_action_dim != 12 or self.raw_state_dim != 16:
-                raise ValueError("atomic_4 requires raw_state_dim=16 and raw_action_dim=12.")
         mapping = G05_EMBODIMENT_MAPPINGS.get(self.embodiment)
         if mapping is not None:
             if len(mapping["state"]) != self.raw_state_dim:

tests/policies/g05/test_g05.py

@@ -237,36 +237,6 @@ def test_select_action_discards_tail_beyond_execution_window():
     assert calls == 2
 
 
-def test_libero_and_atomic4_are_distinct_validated_mappings():
-    with pytest.raises(ValueError, match="27D"):
-        G05Config(
-            checkpoint_profile="custom",
-            embodiment="atomic_4",
-            raw_state_dim=16,
-            raw_action_dim=12,
-            camera_order=(
-                "observation.images.robot0_agentview_left",
-                "observation.images.robot0_eye_in_hand",
-                "observation.images.robot0_agentview_right",
-            ),
-        )
-
-    cfg = G05Config(
-        checkpoint_profile="custom",
-        embodiment="atomic_4",
-        raw_state_dim=16,
-        raw_action_dim=12,
-        policy_state_dim=27,
-        policy_action_dim=27,
-        camera_order=(
-            "observation.images.robot0_agentview_left",
-            "observation.images.robot0_eye_in_hand",
-            "observation.images.robot0_agentview_right",
-        ),
-    )
-    assert cfg.embodiment == "atomic_4"
-
-
 def test_libero_projection_mask_and_inverse_roundtrip():
     config = _config()
     preprocessor, postprocessor = make_pre_post_processors(config)
@@ -331,50 +301,6 @@ def test_lerobot_libero_two_finger_state_matches_author_first_qpos_contract():
     assert torch.equal(processed[OBS_STATE][0, list(checkpoint_slots)], env_state[:7])
 
 
-def test_atomic4_projection_has_mobile_base_control_mode_and_exact_inverse():
-    config = G05Config(
-        checkpoint_profile="custom",
-        embodiment="atomic_4",
-        raw_state_dim=16,
-        raw_action_dim=12,
-        policy_state_dim=27,
-        policy_action_dim=27,
-        normalization_mode="identity",
-        camera_order=(
-            "observation.images.robot0_agentview_left",
-            "observation.images.robot0_eye_in_hand",
-            "observation.images.robot0_agentview_right",
-        ),
-        input_features={
-            OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(16,)),
-            "observation.images.robot0_agentview_left": PolicyFeature(
-                type=FeatureType.VISUAL, shape=(3, 8, 8)
-            ),
-            "observation.images.robot0_eye_in_hand": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 8, 8)),
-            "observation.images.robot0_agentview_right": PolicyFeature(
-                type=FeatureType.VISUAL, shape=(3, 8, 8)
-            ),
-        },
-        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(12,))},
-        device="cpu",
-    )
-    preprocessor, postprocessor = make_pre_post_processors(config)
-    raw_action = torch.arange(12, dtype=torch.float32).repeat(3, 1)
-    batch = {
-        OBS_STATE: torch.arange(16, dtype=torch.float32),
-        ACTION: raw_action,
-        **{camera: torch.zeros(3, 8, 8) for camera in config.camera_order},
-        "task": "atomic",
-    }
-
-    processed = preprocessor(batch)
-    indices = G05_EMBODIMENT_MAPPINGS["atomic_4"]["action"]
-    assert torch.equal(processed[ACTION][..., list(indices)], raw_action)
-    assert torch.equal(postprocessor(processed[ACTION]), raw_action)
-    # Last five raw dimensions are base motion[4] and control mode.
-    assert indices[-5:] == (20, 21, 22, 23, 24)
-
-
 def test_quantile_mode_refuses_minmax_substitution():
     config = _config(normalization_mode="q01_q99")
     stats = {