refactor(eval): integrate preprocessor and postprocessor into rollout and eval_policy functions (#1900)

* refactor(eval): integrate preprocessor and postprocessor into rollout and eval_policy functions

- Updated the `rollout` and `eval_policy` functions to accept preprocessor and postprocessor parameters, enhancing the flexibility of the evaluation pipeline.
- Adjusted the implementation to apply preprocessing and postprocessing steps during policy evaluation, improving the overall data handling and processing flow.

* refactor(eval): remove redundant observation device conversion in rollout function

- Eliminated unnecessary device conversion for the observation dictionary within the `rollout` function, streamlining the code and enhancing readability.
- This change simplifies the observation handling process, aligning with the preference for clearer solutions.

* debug

* refactor(utils): enhance task handling in add_envs_task function

- Improved the `add_envs_task` function to validate the output of `task_description` and `task` calls, ensuring they return lists of strings.
- Removed the use of `else` statement for environments without language instructions, simplifying the logic and enhancing readability.
- Streamlined the observation dictionary handling by ensuring consistent data types for task attributes.

src/lerobot/scripts/train.py

@@ -298,9 +298,11 @@ def train(cfg: TrainPipelineConfig):
                 torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(),
             ):
                 eval_info = eval_policy(
-                    eval_env,
-                    policy,
-                    cfg.eval.n_episodes,
+                    env=eval_env,
+                    policy=policy,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    n_episodes=cfg.eval.n_episodes,
                     videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
                     max_episodes_rendered=4,
                     start_seed=cfg.seed,