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)

tests/test_datasets.py

@@ -1,4 +1,5 @@
 import pytest
+import torch
 
 from lerobot.common.datasets.factory import make_offline_buffer
 
@@ -12,12 +13,18 @@ from .utils import init_config
         # ("simxarm", "lift"),
         ("pusht", "pusht"),
         # TODO(aliberts): add aloha when dataset is available on hub
-        # ("aloha", "sim_insertion_human"),
-        # ("aloha", "sim_insertion_scripted"),
-        # ("aloha", "sim_transfer_cube_human"),
-        # ("aloha", "sim_transfer_cube_scripted"),
+        ("aloha", "sim_insertion_human"),
+        ("aloha", "sim_insertion_scripted"),
+        ("aloha", "sim_transfer_cube_human"),
+        ("aloha", "sim_transfer_cube_scripted"),
     ],
 )
 def test_factory(env_name, dataset_id):
     cfg = init_config(overrides=[f"env={env_name}", f"env.task={dataset_id}"])
     offline_buffer = make_offline_buffer(cfg)
+    for key in offline_buffer.image_keys:
+        img = offline_buffer[0].get(key)
+        assert img.dtype == torch.float32
+        # TODO(rcadene): we assume for now that image normalization takes place in the model
+        assert img.max() <= 1.0
+        assert img.min() >= 0.0