Trim GR00T N1.7 RTC chunks to valid horizon

src/lerobot/policies/groot/modeling_groot.py

@@ -292,6 +292,27 @@ class GrootPolicy(PreTrainedPolicy):
             horizons.append(execution_horizon)
         return min(horizons)
 
+    def _resolve_prediction_horizon(self, actions: Tensor) -> int:
+        """Return the policy-facing action horizon for a native GR00T prediction."""
+
+        if self.config.model_version != GROOT_N1_7:
+            return actions.shape[1]
+
+        horizons = [actions.shape[1]]
+        checkpoint_action_horizon = infer_groot_n1_7_action_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        if checkpoint_action_horizon is not None:
+            horizons.append(checkpoint_action_horizon)
+
+        for horizon in (self.config.chunk_size, self.config.n_action_steps):
+            horizon = int(horizon)
+            if horizon > 0:
+                horizons.append(horizon)
+
+        return max(1, min(horizons))
+
     def _filter_groot_inputs(self, batch: dict[str, Tensor], *, include_action: bool) -> dict[str, Tensor]:
         allowed_base = {"state", "state_mask", "embodiment_id"}
         if include_action:
@@ -455,6 +476,9 @@ class GrootPolicy(PreTrainedPolicy):
 
         actions = outputs.get("action_pred")
 
+        prediction_horizon = self._resolve_prediction_horizon(actions)
+        actions = actions[:, :prediction_horizon]
+
         original_action_dim = self.config.output_features[ACTION].shape[0]
         actions = actions[:, :, :original_action_dim]
 

tests/policies/groot/test_groot_n1_7.py

@@ -458,6 +458,40 @@ def test_groot_predict_action_chunk_forwards_n1_7_rtc_prefix(monkeypatch):
     torch.testing.assert_close(dummy_model.forward_inputs["action"][0, :, 7:], torch.zeros(8, 125))
 
 
+def test_groot_n1_7_predict_action_chunk_truncates_to_checkpoint_valid_horizon(tmp_path, monkeypatch):
+    from lerobot.policies.groot.groot_n1_7 import GR00TN17
+
+    model_path = tmp_path / "libero_spatial"
+    _write_raw_n1_7_libero_checkpoint(model_path)
+
+    class HorizonModel(_DummyGrootModel):
+        def get_action(self, inputs, options=None):
+            del options
+            batch_size = inputs["state"].shape[0]
+            steps = torch.arange(40, dtype=torch.float32).view(1, 40, 1).expand(batch_size, 40, 132)
+            return {"action_pred": steps}
+
+    monkeypatch.setattr(GR00TN17, "from_pretrained", classmethod(lambda cls, **kwargs: HorizonModel()))
+    input_features, output_features = _groot_features(state_dim=8, action_dim=7)
+    config = GrootConfig(
+        model_version=GROOT_N1_7,
+        base_model_path=str(model_path),
+        embodiment_tag="libero_sim",
+        input_features=input_features,
+        output_features=output_features,
+        device="cpu",
+        use_bf16=False,
+        chunk_size=40,
+        n_action_steps=40,
+    )
+    policy = GrootPolicy(config)
+
+    actions = policy.predict_action_chunk({"state": torch.zeros(1, 1, 132)})
+
+    assert actions.shape == (1, 16, 7)
+    torch.testing.assert_close(actions[0, :, 0], torch.arange(16, dtype=torch.float32))
+
+
 def test_groot_from_pretrained_rejects_mismatched_caller_config(tmp_path):
     model_path = tmp_path / "GR00T-N1.7-local"
     model_path.mkdir()