Trim GR00T N1.7 RTC chunks to valid horizon

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()