add stride

src/lerobot/policies/rlearn/configuration_rlearn.py

@@ -55,6 +55,8 @@ class RLearNConfig(PreTrainedConfig):
 
     # Sequence length, amount of past frames including current one to use in the temporal model
     max_seq_len: int = 16
+    # Temporal sampling stride (2 = skip every other frame for wider temporal coverage)
+    temporal_sampling_stride: int = 2
 
     # Training
     learning_rate: float = 1e-3

src/lerobot/policies/rlearn/modeling_rlearn.py

@@ -579,6 +579,7 @@ class RLearNPolicy(PreTrainedPolicy):
             
             # Only apply mismatch loss if we have actual mismatches
             if any(mismatch_mask):
+                print("Applying mismatch loss!!!")
                 # Re-encode with mismatched language
                 lang_embeds_mm, mask_mm = self._encode_language_tokens(shuffled_commands, device)
                 lang_tokens_mm = self.to_lang_tokens(lang_embeds_mm)
@@ -662,10 +663,6 @@ class RLearNPolicy(PreTrainedPolicy):
                         pred_std = sample_preds.std()
                         target_std = sample_targets.std()
                         print(f"  Variation - Target std: {target_std:.4f} | Pred std: {pred_std:.4f}")
-                        if pred_std < 0.01:
-                            print(f"  ⚠️  PREDICTIONS STUCK (std={pred_std:.5f})")
-                        else:
-                            print(f"  ✓ Predictions varying normally")
                     else:
                         # For longer sequences, show first 8 and last 8
                         print(f"  Targets: {sample_targets[:8]} ... {sample_targets[-8:]}")
@@ -897,17 +894,21 @@ class RLearNPolicy(PreTrainedPolicy):
             ep_length = ep_end - ep_start
             episode_lengths.append(ep_length)
             
-            # Choose random anchor - need at least T-1 frames before for [-15..0] window
-            min_anchor = T - 1
+            # Choose random anchor - need enough frames before for stride sampling
+            # For T=16 and stride=2, we need frames [anchor-30, anchor-28, ..., anchor-2, anchor] 
+            stride = self.config.temporal_sampling_stride
+            min_anchor = (T - 1) * stride  # Need (T-1)*stride frames before anchor
             max_anchor = max(min_anchor, ep_length - 1)
             anchor = torch.randint(min_anchor, max_anchor + 1, (1,)).item()
             anchor_positions.append(anchor)
             
-            # Build window indices with reflection padding
+            # Build window indices with configurable stride sampling and reflection padding
             window_indices = []
             frame_indices_for_progress = []  # Track actual frame positions for progress
             had_oob = False
-            for delta in range(-(T-1), 1):  # [-15, -14, ..., 0] for T=16
+            # Sample with stride: [anchor-(T-1)*stride, anchor-(T-2)*stride, ..., anchor-stride, anchor]
+            for i in range(T):
+                delta = -(T - 1 - i) * stride  # Work backwards from anchor with stride spacing
                 idx = anchor + delta
                 actual_frame_idx = idx  # Store the actual frame index before reflection
                 if idx < 0: