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/common/datasets/simxarm.py

@@ -1,4 +1,5 @@
 import pickle
+import zipfile
 from pathlib import Path
 from typing import Callable
 
@@ -15,6 +16,22 @@ from torchrl.data.replay_buffers.writers import Writer
 from lerobot.common.datasets.abstract import AbstractExperienceReplay
 
 
+def download():
+    raise NotImplementedError()
+    import gdown
+
+    url = "https://drive.google.com/uc?id=1nhxpykGtPDhmQKm-_B8zBSywVRdgeVya"
+    download_path = "data.zip"
+    gdown.download(url, download_path, quiet=False)
+    print("Extracting...")
+    with zipfile.ZipFile(download_path, "r") as zip_f:
+        for member in zip_f.namelist():
+            if member.startswith("data/xarm") and member.endswith(".pkl"):
+                print(member)
+                zip_f.extract(member=member)
+    Path(download_path).unlink()
+
+
 class SimxarmExperienceReplay(AbstractExperienceReplay):
     available_datasets = [
         "xarm_lift_medium",
@@ -48,8 +65,8 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
         )
 
     def _download_and_preproc(self):
-        # download
-        # TODO(rcadene)
+        # TODO(rcadene): finish download
+        download()
 
         dataset_path = self.data_dir / "buffer.pkl"
         print(f"Using offline dataset '{dataset_path}'")