format

docs/source/training_time_rtc.mdx

@@ -60,4 +60,3 @@ lerobot-train \
 
 - [Real-Time Chunking (Inference-Time RTC)](./rtc)
 - [Pi0](./pi0), [Pi0.5](./pi05), [SmolVLA](./smolvla), [WALL-OSS](./walloss)
-

src/lerobot/policies/pi0/modeling_pi0.py

@@ -724,9 +724,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         if time_emb.dim() == 2:
             time_emb = time_emb[:, None, :].expand_as(action_emb)
         elif time_emb.shape[:2] != action_emb.shape[:2]:
-            raise ValueError(
+            raise ValueError(f"Expected time_emb shape {action_emb.shape[:2]}, got {time_emb.shape[:2]}")
-                f"Expected time_emb shape {action_emb.shape[:2]}, got {time_emb.shape[:2]}"
-            )
         action_time_emb = torch.cat([action_emb, time_emb], dim=2)
 
         def mlp_func(action_time_emb):

src/lerobot/policies/pi05/modeling_pi05.py

@@ -1270,9 +1270,7 @@ class PI05Policy(PreTrainedPolicy):
             noise = self.model.sample_noise(actions.shape, actions.device)
             delay = sample_rtc_delay(rtc_cfg, batch_size, actions.device)
             time, postfix_mask = apply_rtc_training_time(time, delay, actions.shape[1])
-            losses = self.model.forward(
+            losses = self.model.forward(images, img_masks, tokens, masks, actions, noise=noise, time=time)
-                images, img_masks, tokens, masks, actions, noise=noise, time=time
-            )
         else:
             losses = self.model.forward(images, img_masks, tokens, masks, actions)
 

src/lerobot/policies/rtc/configuration_rtc.py

@@ -69,8 +69,6 @@ class RTCTrainingConfig:
         if self.min_delay < 0:
             raise ValueError(f"min_delay must be >= 0, got {self.min_delay}")
         if self.max_delay < self.min_delay:
-            raise ValueError(
+            raise ValueError(f"max_delay ({self.max_delay}) must be >= min_delay ({self.min_delay})")
-                f"max_delay ({self.max_delay}) must be >= min_delay ({self.min_delay})"
-            )
         if self.exp_decay <= 0:
             raise ValueError(f"exp_decay must be positive, got {self.exp_decay}")

src/lerobot/policies/rtc/training_time.py

@@ -27,9 +27,7 @@ def sample_rtc_delay(cfg: RTCTrainingConfig, batch_size: int, device: torch.devi
         return torch.full((batch_size,), cfg.min_delay, device=device, dtype=torch.long)
 
     if cfg.delay_distribution == RTCTrainingDelayDistribution.UNIFORM:
-        return torch.randint(
+        return torch.randint(cfg.min_delay, cfg.max_delay + 1, (batch_size,), device=device, dtype=torch.long)
-            cfg.min_delay, cfg.max_delay + 1, (batch_size,), device=device, dtype=torch.long
-        )
 
     delay_values = torch.arange(cfg.min_delay, cfg.max_delay + 1, device=device, dtype=torch.long)
     weights = torch.exp(-cfg.exp_decay * delay_values.to(dtype=torch.float32))
@@ -62,4 +60,3 @@ def masked_mean(
     masked = losses * mask
     denom = mask.sum(dim=reduce_dims).clamp_min(eps)
     return masked.sum(dim=reduce_dims) / denom
-

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -751,9 +751,7 @@ class VLAFlowMatching(nn.Module):
         if time_emb.dim() == 2:
             time_emb = time_emb[:, None, :].expand_as(action_emb)
         elif time_emb.shape[:2] != action_emb.shape[:2]:
-            raise ValueError(
+            raise ValueError(f"Expected time_emb shape {action_emb.shape[:2]}, got {time_emb.shape[:2]}")
-                f"Expected time_emb shape {action_emb.shape[:2]}, got {time_emb.shape[:2]}"
-            )
         action_time_emb = torch.cat([action_emb, time_emb], dim=2)
 
         action_time_emb = self.action_time_mlp_in(action_time_emb)

tests/policies/rtc/test_training_time_rtc.py

@@ -48,4 +48,3 @@ def test_apply_rtc_training_time_prefix_mask():
     # Delay=2 means the first two steps are prefix (time forced to 0.0) and only the last two are postfix.
     assert torch.allclose(time_tokens[0], torch.tensor([0.0, 0.0, 0.5, 0.5]))
     assert torch.equal(postfix_mask[0], torch.tensor([False, False, True, True]))
-