Add direct access to action chunks

lerobot/common/policies/act/modeling_act.py

@@ -142,6 +142,37 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor:
             self._action_queue.extend(actions.transpose(0, 1))
         return self._action_queue.popleft()
 
+    @torch.no_grad
+    def predict_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+        """Predict a chunk of actions given environment observations.
+
+        This method returns the raw chunk of actions predicted by the model without
+        any queue management or action consumption logic.
+
+        Args:
+            batch: A dictionary of observation tensors.
+
+        Returns:
+            A tensor of shape (batch_size, chunk_size, action_dim) containing predicted actions.
+        """
+        self.eval()
+
+        batch = self.normalize_inputs(batch)
+        if self.config.image_features:
+            batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
+            batch["observation.images"] = [batch[key] for key in self.config.image_features]
+
+        # If we are using temporal ensembling
+        if self.config.temporal_ensemble_coeff is not None:
+            actions = self.model(batch)[0]  # (batch_size, chunk_size, action_dim)
+            actions = self.unnormalize_outputs({"action": actions})["action"]
+            return actions
+
+        # Standard action prediction
+        actions = self.model(batch)[0]
+        actions = self.unnormalize_outputs({"action": actions})["action"]
+        return actions
+
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)