Merge remote-tracking branch 'origin/main' into user/khalil-meftah/2026-02-16-rl-stack-refactor

# Conflicts:
#	src/lerobot/policies/__init__.py
#	src/lerobot/rl/actor.py

tests/policies/rtc/test_action_interpolator.py

@@ -17,9 +17,9 @@
 import pytest
 import torch
 
-from lerobot.policies.rtc.action_interpolator import ActionInterpolator
 from lerobot.policies.rtc.action_queue import ActionQueue
 from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.utils.action_interpolator import ActionInterpolator
 
 # ====================== Fixtures ======================
 

tests/policies/rtc/test_configuration_rtc.py

@@ -26,7 +26,7 @@ def test_rtc_config_default_initialization():
     """Test RTCConfig initializes with default values."""
     config = RTCConfig()
 
-    assert config.enabled is False
+    assert config.enabled is True
     assert config.prefix_attention_schedule == RTCAttentionSchedule.LINEAR
     assert config.max_guidance_weight == 10.0
     assert config.execution_horizon == 10

tests/policies/rtc/test_rtc_relative_actions.py

@@ -22,7 +22,7 @@ from lerobot.configs.types import (
     PolicyFeature,
     RTCAttentionSchedule,
 )
-from lerobot.processor import TransitionKey, batch_to_transition
+from lerobot.processor import TransitionKey, batch_to_transition, create_transition
 from lerobot.processor.normalize_processor import NormalizerProcessorStep, UnnormalizerProcessorStep
 from lerobot.processor.relative_action_processor import (
     AbsoluteActionsProcessorStep,
@@ -52,6 +52,9 @@ _rtc_debug_mod = _import_rtc_module("lerobot.policies.rtc.debug_tracker", "debug
 _rtc_mod = _import_rtc_module("lerobot.policies.rtc.modeling_rtc", "modeling_rtc.py")
 RTCProcessor = _rtc_mod.RTCProcessor
 
+_rtc_relative_mod = _import_rtc_module("lerobot.policies.rtc.relative", "relative.py")
+reanchor_relative_rtc_prefix = _rtc_relative_mod.reanchor_relative_rtc_prefix
+
 ACTION_DIM = 6
 CHUNK_SIZE = 50
 EXECUTION_HORIZON = 10
@@ -187,7 +190,7 @@ class TestRTCDenoiseWithRelativeLeftovers:
 
 
 class TestFullPipelineRelativeRTC:
-    """End-to-end test of the RTC + relative actions pipeline matching eval_with_real_robot.py flow."""
+    """End-to-end test of the RTC + relative actions pipeline matching lerobot-rollout flow."""
 
     def test_preprocessor_caches_state_for_postprocessor(self):
         """Preprocessor's relative step should cache state so postprocessor can convert back."""
@@ -218,7 +221,9 @@ class TestFullPipelineRelativeRTC:
 
     def test_roundtrip_with_identity_normalization(self):
         """Actions → relative → normalize → [model] → unnormalize → absolute should recover originals.
-        Using mean=0, std=1 normalization (identity)."""
+
+        Using mean=0, std=1 normalization (identity).
+        """
         relative_step, normalizer, unnormalizer, absolute_step = _make_relative_pipeline()
 
         state = torch.randn(1, ACTION_DIM)
@@ -240,7 +245,7 @@ class TestFullPipelineRelativeRTC:
         torch.testing.assert_close(recovered, actions, atol=1e-5, rtol=1e-5)
 
     def test_eval_loop_simulation(self):
-        """Simulate the eval_with_real_robot.py loop with relative actions.
+        """Simulate the lerobot-rollout loop with relative actions.
 
         Iteration 1: No leftovers → model generates relative actions → store for RTC
         Iteration 2: Use leftovers as RTC guidance → model generates new relative actions
@@ -400,13 +405,113 @@ class TestStateRebasingApproximation:
         assert error_excluded < 1e-6, f"Excluded joint should have zero error, got {error_excluded}"
 
 
+class TestRTCReanchoringWithStateNormalizer:
+    """RTC re-anchoring under non-identity OBS_STATE normalization."""
+
+    @staticmethod
+    def _build_normalizer_with_state_stats():
+        """Build a relative-action preprocessor with non-trivial OBS_STATE stats."""
+        features = {
+            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(ACTION_DIM,)),
+            OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(ACTION_DIM,)),
+        }
+        norm_map = {
+            FeatureType.ACTION: NormalizationMode.MEAN_STD,
+            FeatureType.STATE: NormalizationMode.MEAN_STD,
+        }
+        stats = {
+            ACTION: {
+                "mean": torch.zeros(ACTION_DIM).numpy(),
+                "std": (0.5 * torch.ones(ACTION_DIM)).numpy(),
+            },
+            OBS_STATE: {
+                "mean": (5.0 * torch.ones(ACTION_DIM)).numpy(),
+                "std": (2.0 * torch.ones(ACTION_DIM)).numpy(),
+            },
+        }
+        relative_step = RelativeActionsProcessorStep(enabled=True)
+        normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats)
+        return relative_step, normalizer
+
+    def test_reanchor_with_raw_state_matches_normalize_of_absolute_minus_state(self):
+        """Reanchoring with the raw cached state yields ``normalize(prev_actions_absolute - raw_state)``."""
+        relative_step, normalizer = self._build_normalizer_with_state_stats()
+
+        raw_state = torch.tensor([[7.0, 8.0, 9.0, 10.0, 11.0, 12.0]])
+        relative_step(batch_to_transition({OBS_STATE: raw_state.clone()}))
+
+        prev_actions_absolute = torch.tensor([[2.0, 3.0, 4.0, 5.0, 6.0, 7.0]] * 5)
+
+        result = reanchor_relative_rtc_prefix(
+            prev_actions_absolute=prev_actions_absolute,
+            current_state=relative_step.get_cached_state(),
+            relative_step=relative_step,
+            normalizer_step=normalizer,
+            policy_device="cpu",
+        )
+
+        expected_relative = to_relative_actions(prev_actions_absolute, raw_state, [True] * ACTION_DIM)
+        expected = normalizer(create_transition(action=expected_relative))[TransitionKey.ACTION]
+        torch.testing.assert_close(result, expected, atol=1e-5, rtol=1e-5)
+
+    def test_reanchor_with_normalized_state_produces_wrong_result(self):
+        """Reanchoring with raw vs. normalized state produces meaningfully different outputs."""
+        relative_step, normalizer = self._build_normalizer_with_state_stats()
+
+        raw_state = torch.tensor([[7.0, 8.0, 9.0, 10.0, 11.0, 12.0]])
+        relative_step(batch_to_transition({OBS_STATE: raw_state.clone()}))
+
+        normalized_obs = normalizer(batch_to_transition({OBS_STATE: raw_state.clone()}))
+        normalized_state = normalized_obs[TransitionKey.OBSERVATION][OBS_STATE]
+        assert not torch.allclose(normalized_state, raw_state)
+
+        prev_actions_absolute = torch.tensor([[2.0, 3.0, 4.0, 5.0, 6.0, 7.0]] * 5)
+
+        result_raw = reanchor_relative_rtc_prefix(
+            prev_actions_absolute=prev_actions_absolute,
+            current_state=raw_state,
+            relative_step=relative_step,
+            normalizer_step=normalizer,
+            policy_device="cpu",
+        )
+        result_normalized = reanchor_relative_rtc_prefix(
+            prev_actions_absolute=prev_actions_absolute,
+            current_state=normalized_state,
+            relative_step=relative_step,
+            normalizer_step=normalizer,
+            policy_device="cpu",
+        )
+
+        max_abs_diff = (result_raw - result_normalized).abs().max()
+        assert max_abs_diff > 0.5, (
+            f"Raw and normalized state produced near-identical outputs (max diff {max_abs_diff:.4f}); "
+            "OBS_STATE stats are too close to identity to be sensitive."
+        )
+
+    def test_engine_pipeline_cached_state_is_raw_after_full_preprocess(self):
+        """``get_cached_state()`` returns raw OBS_STATE after the full preprocessor pipeline runs."""
+        relative_step, normalizer = self._build_normalizer_with_state_stats()
+
+        raw_state = torch.tensor([[7.0, 8.0, 9.0, 10.0, 11.0, 12.0]])
+
+        transition = batch_to_transition({OBS_STATE: raw_state.clone()})
+        transition = relative_step(transition)
+        preprocessed = normalizer(transition)
+
+        cached = relative_step.get_cached_state()
+        torch.testing.assert_close(cached, raw_state, atol=1e-6, rtol=1e-6)
+
+        post_normalize_state = preprocessed[TransitionKey.OBSERVATION][OBS_STATE]
+        assert not torch.allclose(cached, post_normalize_state, atol=1e-3)
+
+
 def _detect_relative_actions(preprocessor) -> bool:
-    """Mirror of the helper in eval_with_real_robot.py for testing without importing it."""
+    """Mirror of the helper in lerobot-rollout for testing without importing it."""
     return any(isinstance(step, RelativeActionsProcessorStep) and step.enabled for step in preprocessor.steps)
 
 
 class TestDetectRelativeActions:
-    """Test the _detect_relative_actions helper logic used by eval_with_real_robot.py."""
+    """Test the _detect_relative_actions helper logic used by lerobot-rollout."""
 
     def test_detects_enabled_relative_step(self):
         class FakePipeline:

tests/policies/test_policies.py

@@ -196,6 +196,8 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
 
     for key in batch:
         if isinstance(batch[key], torch.Tensor):
+            if batch[key].dtype == torch.uint8:
+                batch[key] = batch[key].to(dtype=torch.float32) / 255.0
             batch[key] = batch[key].to(DEVICE, non_blocking=True)
 
     # Test updating the policy (and test that it does not mutate the batch)