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/envs/abstract.py

@@ -0,0 +1,80 @@
+import abc
+from collections import deque
+from typing import Optional
+
+from tensordict import TensorDict
+from torchrl.envs import EnvBase
+
+
+class AbstractEnv(EnvBase):
+    def __init__(
+        self,
+        task,
+        frame_skip: int = 1,
+        from_pixels: bool = False,
+        pixels_only: bool = False,
+        image_size=None,
+        seed=1337,
+        device="cpu",
+        num_prev_obs=1,
+        num_prev_action=0,
+    ):
+        super().__init__(device=device, batch_size=[])
+        self.task = task
+        self.frame_skip = frame_skip
+        self.from_pixels = from_pixels
+        self.pixels_only = pixels_only
+        self.image_size = image_size
+        self.num_prev_obs = num_prev_obs
+        self.num_prev_action = num_prev_action
+        self._rendering_hooks = []
+
+        if pixels_only:
+            assert from_pixels
+        if from_pixels:
+            assert image_size
+
+        self._make_env()
+        self._make_spec()
+        self._current_seed = self.set_seed(seed)
+
+        if self.num_prev_obs > 0:
+            self._prev_obs_image_queue = deque(maxlen=self.num_prev_obs)
+            self._prev_obs_state_queue = deque(maxlen=self.num_prev_obs)
+        if self.num_prev_action > 0:
+            raise NotImplementedError()
+            # self._prev_action_queue = deque(maxlen=self.num_prev_action)
+
+    def register_rendering_hook(self, func):
+        self._rendering_hooks.append(func)
+
+    def call_rendering_hooks(self):
+        for func in self._rendering_hooks:
+            func(self)
+
+    def reset_rendering_hooks(self):
+        self._rendering_hooks = []
+
+    @abc.abstractmethod
+    def render(self, mode="rgb_array", width=640, height=480):
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def _reset(self, tensordict: Optional[TensorDict] = None):
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def _step(self, tensordict: TensorDict):
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def _make_env(self):
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def _make_spec(self):
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def _set_seed(self, seed: Optional[int]):
+        raise NotImplementedError()