Add direct access to action chunks (#1020)

* fix: sharing predicted chunk with user

* [pre-commit.ci] pre-commit autoupdate (#1011)

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* Revert "[pre-commit.ci] pre-commit autoupdate" (#1025)

* fix(ci): Pin draccus (<0.10.0) and torch (<2.7) to fix pipeline (#1022)

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* fix(ci): Pin `torchcodec` (==0.2.1) to fix pipeline temporarly (#1030)

* Update tutorial (#1021)

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* Add description motor order SO-101 leader (#1051)

* feat(encoding): switching to PyAV for ffmpeg related tasks (#983)

* feat(docs): Add new docs build process (#1046)

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* Docs: adapt text + fix video code (#1064)

* Fix typos (#1070)

* docs: minor corrections and clean-up (#1089)

* Update 10_use_so100.md; use diff syntax (#944)

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* Update 12_use_so101.md (#1081)

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* bug fix for #1071 When --display_data=true, Failed running control_robot. (#1073)

* Add editable -e for feetech install command (#1133)

* Fix: emptying action queue between resets (#1117)

* fix: typos and grammar (#1148)

* Update README.md (#1160)

* Update README.md (#1163)

* [Fix]  Unpin torch beyond 2.6.0 & torchcodec beyond 0.2.1  (#1127)

* (hotfix): nightly CI by clipping pymunk version below 7.0.0 (#1182)

* [pre-commit.ci] pre-commit autoupdate (#1048)

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* Add SmolVLA (#1175)

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* Fix SmolVLA loss not sent to wandb (#1198)

* Hardware API redesign (#777)

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* fix(smolvla): update record.py, fix populate_queues and remove unused dependencies (#1208)

* replaced OBS_ROBOT with OBS_STATE constant (#1211)

* Fix test_teleoperate (#1216)

* Fix LeKiwi example (#1217)

* Fix smolVLA dependencies (#1218)

* fix(pyserial): adding pyserial dependency to global ones (#1219)

* Update SmolVLA README.md (#1228)

* Fix unable to set camera width/height to non-default (#1225)

* Update tutorial link (#1250)

* update KochFollower.get_observation() so it returns same observation structure as SO101 (#1248)

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* [pre-commit.ci] pre-commit autoupdate (#1185)

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* Proposal for fix for enter_pressed on Windows (#1230)

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* fix: update pi0 dependency version constraint (#1247)

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* Match motor names with ids lekiwi (#1261)

* fix issues: checkpoints keys mismatch and 'task' tokenisation in smolvla (#1256)

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* fix(docs): update realsense documentation (#1268)

* Use HF Papers (#1120)

* Skip normalization parameters in load_smolvla (#1274)

* fix(record): no teleop needed when running with policy (#1284)

* Port HIL SERL (#644)

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* fix(docs): SmolVLA fine-tuning getting started (#1201)

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* chore(teleop): print calibration path saved (#1286)

* chore(dependencies): add gamepad support with pygame and hidapi (#1287)

* Robot integration tutorial (#1285)

* fix(docs): update send_feedback docstrings

* Add sim tutorial, fix lekiwi motor config, add notebook links (#1275)

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* Fixes on robot integration tutorial (#1290)

* Add keyboard teleop device to control the end effector robot  (#1289)

* Improve type hints (#1293)

* fix(record): no teleop arg in reset environment (#1294)

* `learner.py` import so101_leader instead of so100 (#1295)

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* Fixing `PI0` Policy (#1297)

* `gym_manipulator.py` Remove None value action_intervention of BaseLeaderTeleoperator (#1299)

* (chore): incorrect resume parameter in recording documentation (#1301)

* Update lekiwi.mdx  (#1229)

* bump `pi0` and `hil` transformers version (#1298)

* docs: fix imitation learning robots docs command (#1308)

* fix(benchmarks): remove .numpy() from frame in benchmark script (#1354)

* add smolvla to the supported policies to run tests (:

* add: chunk-level access for the policy

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* add: smolvla in availables

* remove: smolvla from library supported policies

* fix: change env for training, xarm is broken as of now

* add: predict_action_chunk to all supported policies

* fix: add robot type constants

* add: predict action chunk in base policy class

* restore original Makefile

* fix: minor

* fix: dict keys come from lerobot/constants

* fix: improve act encapsulation, properly supporting temporal ensembling

* fix: smolvla action chunking

* fix: very minor, but very annoying

* fix: minor

* fix minor naming

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* fix: refactoring inference for single actions and chunks into different components

* fix: minor

* fix: temporal ensembling

* fix: moving populate queues out of modular component for batch preparation

* fix: minor for CI

* fix: smovla debug

* fix: reward classifier, maybe the last policy lacking?

---------

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lerobot/common/policies/sac/modeling_sac.py

@@ -76,6 +76,11 @@ class SACPolicy(
         """Reset the policy"""
         pass
 
+    @torch.no_grad
+    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+        """Predict a chunk of actions given environment observations."""
+        raise NotImplementedError("SACPolicy does not support action chunking. It returns single actions!")
+
     @torch.no_grad()
     def select_action(self, batch: dict[str, Tensor]) -> Tensor:
         """Select action for inference/evaluation"""

lerobot/common/policies/sac/reward_model/modeling_classifier.py

@@ -308,6 +308,13 @@ class Classifier(PreTrainedPolicy):
         """
         raise NotImplementedError("Reward classifiers do not select actions")
 
+    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+        """
+        This method is required by PreTrainedPolicy but not used for reward classifiers.
+        The reward classifier is not an actor and does not produce action chunks.
+        """
+        raise NotImplementedError("Reward classifiers do not predict action chunks")
+
     def reset(self):
         """
         This method is required by PreTrainedPolicy but not used for reward classifiers.