feat(envs): add envs pre-post processor (#2474)

* more changes

* working changes

* more changes

* more fixes

* fix style

* more

* clean

* put axis-1

* more fixes

* more styling fixes:

* iterate on review:

* more changes

* add env processor

* style

* more changes

* add docs

* fix imports

* fix test, add to train

* Update src/lerobot/envs/factory.py

Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Signed-off-by: Jade Choghari <chogharijade@gmail.com>

* iterate on review

---------

Signed-off-by: Jade Choghari <chogharijade@gmail.com>
Co-authored-by: jade.choghari@huggingface.co <“chogharijade@gmail.com”>
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>

src/lerobot/scripts/lerobot_eval.py

@@ -71,7 +71,7 @@ from tqdm import trange
 
 from lerobot.configs import parser
 from lerobot.configs.eval import EvalPipelineConfig
-from lerobot.envs.factory import make_env
+from lerobot.envs.factory import make_env, make_env_pre_post_processors
 from lerobot.envs.utils import (
     add_envs_task,
     check_env_attributes_and_types,
@@ -94,6 +94,8 @@ from lerobot.utils.utils import (
 def rollout(
     env: gym.vector.VectorEnv,
     policy: PreTrainedPolicy,
+    env_preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    env_postprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction],
     seeds: list[int] | None = None,
@@ -165,11 +167,19 @@ def rollout(
         # Infer "task" from attributes of environments.
         # TODO: works with SyncVectorEnv but not AsyncVectorEnv
         observation = add_envs_task(env, observation)
+
+        # Apply environment-specific preprocessing (e.g., LiberoProcessorStep for LIBERO)
+        observation = env_preprocessor(observation)
+
         observation = preprocessor(observation)
         with torch.inference_mode():
             action = policy.select_action(observation)
         action = postprocessor(action)
 
+        action_transition = {"action": action}
+        action_transition = env_postprocessor(action_transition)
+        action = action_transition["action"]
+
         # Convert to CPU / numpy.
         action_numpy: np.ndarray = action.to("cpu").numpy()
         assert action_numpy.ndim == 2, "Action dimensions should be (batch, action_dim)"
@@ -239,6 +249,8 @@ def rollout(
 def eval_policy(
     env: gym.vector.VectorEnv,
     policy: PreTrainedPolicy,
+    env_preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    env_postprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction],
     n_episodes: int,
@@ -319,6 +331,8 @@ def eval_policy(
         rollout_data = rollout(
             env=env,
             policy=policy,
+            env_preprocessor=env_preprocessor,
+            env_postprocessor=env_postprocessor,
             preprocessor=preprocessor,
             postprocessor=postprocessor,
             seeds=list(seeds) if seeds else None,
@@ -517,10 +531,16 @@ def eval_main(cfg: EvalPipelineConfig):
         pretrained_path=cfg.policy.pretrained_path,
         preprocessor_overrides=preprocessor_overrides,
     )
+
+    # Create environment-specific preprocessor and postprocessor (e.g., for LIBERO environments)
+    env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env)
+
     with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext():
         info = eval_policy_all(
             envs=envs,
             policy=policy,
+            env_preprocessor=env_preprocessor,
+            env_postprocessor=env_postprocessor,
             preprocessor=preprocessor,
             postprocessor=postprocessor,
             n_episodes=cfg.eval.n_episodes,
@@ -561,6 +581,8 @@ def eval_one(
     env: gym.vector.VectorEnv,
     *,
     policy: PreTrainedPolicy,
+    env_preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    env_postprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction],
     n_episodes: int,
@@ -576,6 +598,8 @@ def eval_one(
     task_result = eval_policy(
         env=env,
         policy=policy,
+        env_preprocessor=env_preprocessor,
+        env_postprocessor=env_postprocessor,
         preprocessor=preprocessor,
         postprocessor=postprocessor,
         n_episodes=n_episodes,
@@ -600,6 +624,8 @@ def run_one(
     env,
     *,
     policy,
+    env_preprocessor,
+    env_postprocessor,
     preprocessor,
     postprocessor,
     n_episodes: int,
@@ -622,6 +648,8 @@ def run_one(
     metrics = eval_one(
         env,
         policy=policy,
+        env_preprocessor=env_preprocessor,
+        env_postprocessor=env_postprocessor,
         preprocessor=preprocessor,
         postprocessor=postprocessor,
         n_episodes=n_episodes,
@@ -639,6 +667,8 @@ def run_one(
 def eval_policy_all(
     envs: dict[str, dict[int, gym.vector.VectorEnv]],
     policy,
+    env_preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    env_postprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction],
     n_episodes: int,
@@ -694,6 +724,8 @@ def eval_policy_all(
     task_runner = partial(
         run_one,
         policy=policy,
+        env_preprocessor=env_preprocessor,
+        env_postprocessor=env_postprocessor,
         preprocessor=preprocessor,
         postprocessor=postprocessor,
         n_episodes=n_episodes,

src/lerobot/scripts/lerobot_train.py

@@ -29,7 +29,7 @@ from lerobot.configs.train import TrainPipelineConfig
 from lerobot.datasets.factory import make_dataset
 from lerobot.datasets.sampler import EpisodeAwareSampler
 from lerobot.datasets.utils import cycle
-from lerobot.envs.factory import make_env
+from lerobot.envs.factory import make_env, make_env_pre_post_processors
 from lerobot.envs.utils import close_envs
 from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies.factory import make_policy, make_pre_post_processors
@@ -259,6 +259,8 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
         if cfg.env is not None:
             logging.info(f"{cfg.env.task=}")
+            logging.info("Creating environment processors")
+            env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env)
         logging.info(f"{cfg.steps=} ({format_big_number(cfg.steps)})")
         logging.info(f"{dataset.num_frames=} ({format_big_number(dataset.num_frames)})")
         logging.info(f"{dataset.num_episodes=}")
@@ -385,6 +387,8 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
                     eval_info = eval_policy_all(
                         envs=eval_env,  # dict[suite][task_id] -> vec_env
                         policy=accelerator.unwrap_model(policy),
+                        env_preprocessor=env_preprocessor,
+                        env_postprocessor=env_postprocessor,
                         preprocessor=preprocessor,
                         postprocessor=postprocessor,
                         n_episodes=cfg.eval.n_episodes,