Add Aloha env and ACT policy

WIP Aloha env tests pass

Rendering works (fps look fast tho? TODO action bounding is too wide [-1,1])

Update README

Copy past from act repo

Remove download.py add a WIP for Simxarm

Remove download.py add a WIP for Simxarm

Add act yaml (TODO: try train.py)

Training can runs (TODO: eval)

Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model)

Add AbstractEnv, Refactor AlohaEnv, Add rendering_hook in env, Minor modifications, (TODO: Refactor Pusht and Simxarm)

poetry lock

fix bug in compute_stats for action normalization

fix more bugs in normalization

fix training

fix import

PushtEnv inheriates AbstractEnv, Improve factory Normalization

Add _make_env to EnvAbstract

Add call_rendering_hooks to pusht env

SimxarmEnv inherites from AbstractEnv (NOT TESTED)

Add aloha tests artifacts + update pusht stats

fix image normalization: before env was in [0,1] but dataset in [0,255], and now both in [0,255]

Small fix on simxarm

Add next to obs

Add top camera to Aloha env (TODO: make it compatible with set of cameras)

Add top camera to Aloha env (TODO: make it compatible with set of cameras)

lerobot/scripts/train.py

@@ -1,4 +1,5 @@
 import logging
+from pathlib import Path
 
 import hydra
 import numpy as np
@@ -192,6 +193,8 @@ def train(cfg: dict, out_dir=None, job_name=None):
                 num_episodes=cfg.eval_episodes,
                 max_steps=cfg.env.episode_length // cfg.n_action_steps,
                 return_first_video=True,
+                video_dir=Path(out_dir) / "eval",
+                save_video=True,
             )
             log_eval_info(logger, eval_info, step, cfg, offline_buffer, is_offline)
             if cfg.wandb.enable: