Fix GROOT relative action padding and RTC leftovers

src/lerobot/policies/groot/modeling_groot.py

@@ -73,6 +73,7 @@ class GrootPolicy(PreTrainedPolicy):
         # Initialize GR00T model using ported components
         self._groot_model = self._create_groot_model()
         self._action_queue_steps = self._resolve_action_queue_steps()
+        self._warned_native_relative_rtc_prefix_disabled = False
 
         self.reset()
 
@@ -306,6 +307,17 @@ class GrootPolicy(PreTrainedPolicy):
     ) -> tuple[dict[str, Tensor], dict[str, object] | None]:
         if prev_chunk_left_over is None:
             return inputs, None
+        if getattr(self.config, "use_relative_actions", False):
+            # Generic RTC only provides normalized leftovers from the previous chunk. For
+            # native relative-action N1.7 checkpoints those rows are tied to the old
+            # observation state and old per-horizon stats row, so using them as the next
+            # prefix can push the policy in the wrong direction. Run without native RTC
+            # overlap guidance until a GROOT-specific RTC path can pass re-anchored
+            # absolute leftovers through.
+            if not getattr(self, "_warned_native_relative_rtc_prefix_disabled", False):
+                logger.info("Disabling native GR00T RTC prefix for relative-action policy")
+                self._warned_native_relative_rtc_prefix_disabled = True
+            return inputs, None
         if not isinstance(prev_chunk_left_over, torch.Tensor):
             raise TypeError("prev_chunk_left_over must be a torch.Tensor for GR00T N1.7 RTC.")
         if prev_chunk_left_over.numel() == 0:

src/lerobot/policies/groot/processor_groot.py

@@ -588,6 +588,9 @@ def _slice_stats_entry(stats: dict[str, Any], indices: list[int]) -> dict[str, A
     max_index = max(indices)
     sliced: dict[str, Any] = {}
     for stat_name, value in stats.items():
+        if stat_name == "count":
+            sliced[stat_name] = torch.as_tensor(value).flatten().tolist()
+            continue
         tensor = torch.as_tensor(value, dtype=torch.float32)
         if tensor.ndim >= 2:
             if tensor.shape[-1] <= max_index:
@@ -1537,13 +1540,34 @@ class GrootN17PackInputsStep(ProcessorStep):
                 )
                 action = torch.cat([action, pad], dim=1)
                 horizon = self.action_horizon
-            if valid_horizon < horizon:
+            horizon_valid = torch.zeros(bsz, horizon, dtype=torch.bool, device=action.device)
+            horizon_valid[:, :valid_horizon] = True
+            action_is_pad = comp.get(f"{ACTION}_is_pad")
+            if action_is_pad is None:
+                action_is_pad = comp.get("action_horizon_is_pad")
+            if action_is_pad is not None:
+                action_pad = torch.as_tensor(action_is_pad, dtype=torch.bool, device=action.device)
+                if action_pad.ndim == 1:
+                    if bsz == 1 and action_pad.numel() == horizon:
+                        action_pad = action_pad.unsqueeze(0)
+                    elif horizon == 1 and action_pad.numel() == bsz:
+                        action_pad = action_pad.view(bsz, 1)
+                if action_pad.ndim != 2 or action_pad.shape[0] != bsz:
+                    raise ValueError(
+                        "action_is_pad must have shape (B, T) matching the action batch; "
+                        f"got {tuple(action_pad.shape)} for action {tuple(action.shape)}."
+                    )
+                pad_horizon = min(horizon, action_pad.shape[1])
+                horizon_valid[:, :pad_horizon] &= ~action_pad[:, :pad_horizon]
+
+            if valid_horizon < horizon or action_is_pad is not None:
                 action = action.clone()
                 action[:, valid_horizon:, :] = 0
+                action = action * horizon_valid.unsqueeze(-1).to(dtype=action.dtype)
             action_mask = torch.zeros(
                 bsz, horizon, self.max_action_dim, dtype=torch.float32, device=action.device
             )
-            action_mask[:, :valid_horizon, :valid_dim] = 1.0
+            action_mask[:, :, :valid_dim] = horizon_valid.unsqueeze(-1).to(dtype=action_mask.dtype)
             transition[TransitionKey.ACTION] = action
             comp["action_mask"] = action_mask
 

tests/policies/groot/test_groot_n1_7.py

@@ -1074,6 +1074,22 @@ def test_groot_n1_7_pack_inputs_trains_native_relative_groups_with_absolute_grip
     torch.testing.assert_close(output[TransitionKey.ACTION], expected_actions)
 
 
+def test_groot_policy_ignores_rtc_leftovers_for_relative_actions():
+    policy = object.__new__(GrootPolicy)
+    policy.config = SimpleNamespace(use_relative_actions=True)
+    policy._warned_native_relative_rtc_prefix_disabled = False
+    inputs = {"state": torch.zeros(1, 1, 132)}
+
+    output_inputs, options = policy._prepare_n1_7_rtc_inputs(
+        inputs,
+        inference_delay=1,
+        prev_chunk_left_over=torch.ones(8, 6),
+    )
+
+    assert output_inputs is inputs
+    assert options is None
+
+
 def test_groot_n1_7_pack_inputs_adds_inference_action_horizon_mask():
     step = GrootN17PackInputsStep(
         action_horizon=40,
@@ -1098,6 +1114,49 @@ def test_groot_n1_7_pack_inputs_adds_inference_action_horizon_mask():
     assert output[TransitionKey.COMPLEMENTARY_DATA]["embodiment_id"].dtype == torch.int32
 
 
+def test_groot_n1_7_pack_inputs_masks_padded_action_horizons():
+    step = GrootN17PackInputsStep(
+        action_horizon=4,
+        valid_action_horizon=4,
+        max_state_dim=3,
+        max_action_dim=5,
+        normalize_min_max=False,
+    )
+    action = torch.arange(2 * 4 * 3, dtype=torch.float32).view(2, 4, 3)
+    action_is_pad = torch.tensor(
+        [
+            [False, True, False, True],
+            [True, False, False, False],
+        ]
+    )
+    transition = {
+        TransitionKey.OBSERVATION: {
+            OBS_STATE: torch.zeros(2, 3),
+        },
+        TransitionKey.ACTION: action.clone(),
+        TransitionKey.COMPLEMENTARY_DATA: {
+            "task": ["Move", "Place"],
+            "action_is_pad": action_is_pad,
+        },
+    }
+
+    output = step(transition)
+
+    expected_valid = (~action_is_pad).float()
+    action_mask = output[TransitionKey.COMPLEMENTARY_DATA]["action_mask"]
+    assert action_mask.shape == (2, 4, 5)
+    torch.testing.assert_close(action_mask[..., :3], expected_valid.unsqueeze(-1).expand(-1, -1, 3))
+    assert action_mask[..., 3:].sum().item() == 0
+
+    packed_action = output[TransitionKey.ACTION]
+    assert packed_action.shape == (2, 4, 5)
+    torch.testing.assert_close(packed_action[0, 0, :3], action[0, 0])
+    torch.testing.assert_close(packed_action[0, 2, :3], action[0, 2])
+    assert packed_action[0, 1].abs().sum().item() == 0
+    assert packed_action[0, 3].abs().sum().item() == 0
+    assert packed_action[1, 0].abs().sum().item() == 0
+
+
 def test_groot_n1_7_pack_inputs_orders_video_by_checkpoint_modality_keys():
     step = GrootN17PackInputsStep(
         normalize_min_max=False,
@@ -1904,6 +1963,7 @@ def test_groot_n1_7_relative_action_training_processors_save_native_grouped_stat
         [-2.0, -3.0, -4.0, -5.0, -6.0],
         [1.0, 2.0, 3.0, 4.0, 5.0],
     ]
+    assert pack_config["raw_stats"]["relative_action"]["single_arm"]["count"] == [2, 2]
     assert pack_config["raw_stats"]["action"]["gripper"]["min"] == [0.0]
     assert pack_config["raw_stats"]["action"]["gripper"]["max"] == [100.0]
 
@@ -1926,6 +1986,7 @@ def test_groot_n1_7_relative_action_training_processors_save_native_grouped_stat
         [-1.0, -2.0, -3.0, -4.0, -5.0],
         [2.0, 3.0, 4.0, 5.0, 6.0],
     ]
+    assert decode_config["raw_stats"]["relative_action"]["single_arm"]["count"] == [2, 2]
     assert decode_config["raw_stats"]["action"]["gripper"]["max"] == [100.0]