Ignore padded GR00T N1.7 RTC prefix rows

src/lerobot/policies/groot/modeling_groot.py

@@ -373,6 +373,17 @@ class GrootPolicy(PreTrainedPolicy):
                 "prev_chunk_left_over batch size must match the current GR00T N1.7 batch size."
             )
 
+        # The generic LeRobot RTC engine pads short leftovers with exact zero
+        # rows for fixed-shape policy calls. Native GR00T N1.7 RTC treats every
+        # provided prefix row as a real action constraint, so strip that padding
+        # before constructing the native overlap options.
+        valid_prefix_rows = prev_actions.detach().abs().sum(dim=(0, 2)) > 0
+        if valid_prefix_rows.any():
+            valid_prefix_steps = int(valid_prefix_rows.nonzero()[-1].item()) + 1
+            prev_actions = prev_actions[:, :valid_prefix_steps, :]
+        else:
+            return inputs, None
+
         model_action_horizon = int(getattr(self._groot_model.config, "action_horizon", self.config.chunk_size))
         max_action_dim = int(getattr(self._groot_model.config, "max_action_dim", self.config.max_action_dim))
         if prev_actions.shape[1] > model_action_horizon:

tests/policies/groot/test_groot_n1_7.py

@@ -458,6 +458,39 @@ 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_predict_action_chunk_strips_padded_n1_7_rtc_prefix(monkeypatch):
+    from lerobot.policies.groot.groot_n1_7 import GR00TN17
+
+    dummy_model = _DummyGrootModel()
+    monkeypatch.setattr(GR00TN17, "from_pretrained", classmethod(lambda cls, **kwargs: dummy_model))
+    config = _groot_config(GROOT_N1_7)
+    policy = GrootPolicy(config)
+    policy.config.rtc_config = SimpleNamespace(execution_horizon=6)
+
+    prev_chunk = torch.cat(
+        (
+            torch.arange(4 * 7, dtype=torch.float32).view(4, 7) + 1.0,
+            torch.zeros(2, 7),
+        )
+    )
+
+    policy.predict_action_chunk(
+        {"state": torch.zeros(1, 1, 132)},
+        inference_delay=5,
+        prev_chunk_left_over=prev_chunk,
+    )
+
+    assert dummy_model.get_action_options == {
+        "action_horizon": 4,
+        "rtc_overlap_steps": 4,
+        "rtc_frozen_steps": 4,
+        "rtc_ramp_rate": 6.0,
+    }
+    assert dummy_model.forward_inputs["action"].shape == (1, 4, 132)
+    torch.testing.assert_close(dummy_model.forward_inputs["action"][0, :, :7], prev_chunk[:4])
+    torch.testing.assert_close(dummy_model.forward_inputs["action"][0, :, 7:], torch.zeros(4, 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