hls_gaus true

src/lerobot/policies/rlearn/configuration_rlearn.py

@@ -102,10 +102,11 @@ class RLearNConfig(PreTrainedConfig):
 
     @property
     def observation_delta_indices(self) -> list | None:
-        # Use temporal sequences: past frames from -(max_seq_len-1) to current (0)
+        # Request a long enough context so in-window stride sampling can be >1.
-        # This gives us max_seq_len frames total, e.g. [-3, -2, -1, 0] for max_seq_len=4
+        # We ask for (max_seq_len * temporal_sampling_stride) frames ending at t=0.
-        # The dataset will handle padding/repeating frames for episodes shorter than this
+        # Example: max_seq_len=16, temporal_sampling_stride=3 → 48 deltas → ~46 frames available.
-        return list(range(1 - self.max_seq_len, 1))
+        total_needed = self.max_seq_len * max(1, int(self.temporal_sampling_stride))
+        return list(range(1 - total_needed, 1))
 
     @property
     def action_delta_indices(self) -> list | None:

src/lerobot/policies/rlearn/modeling_rlearn.py

@@ -792,16 +792,16 @@ class RLearNPolicy(PreTrainedPolicy):
             ep_length = ep_end - ep_start
             episode_lengths.append(ep_length)
             
-            # Proper window-relative stride sampling within available frames
+            # Proper window-relative stride sampling; allow clamping for out-of-bounds
-            stride = self.config.temporal_sampling_stride
+            effective_stride = max(1, int(self.config.temporal_sampling_stride))
-            # Ensure we have room for T frames at given stride; shrink stride if needed
-            if available_T <= 1:
-                effective_stride = 1
-            else:
-                effective_stride = max(1, min(stride, (available_T - 1) // max(T - 1, 1) if (T - 1) > 0 else 1))
             min_anchor_in_window = (T - 1) * effective_stride
-            max_anchor_in_window = max(min_anchor_in_window, available_T - 1)
+            max_anchor_in_window = available_T - 1
-            anchor_in_window = torch.randint(min_anchor_in_window, max_anchor_in_window + 1, (1,)).item()
+            if min_anchor_in_window <= max_anchor_in_window:
+                anchor_in_window = torch.randint(min_anchor_in_window, max_anchor_in_window + 1, (1,)).item()
+            else:
+                # Not enough frames to satisfy stride; anchor at the last available frame,
+                # earlier indices will clamp to 0 (repeat first frame)
+                anchor_in_window = max_anchor_in_window
 
             # Convert window-anchor to episode-anchor (absolute frame index within episode)
             cur_frame_idx = frame_indices[b_idx].item()