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/pusht/env.py

@@ -11,61 +11,52 @@ from torchrl.data.tensor_specs import (
     DiscreteTensorSpec,
     UnboundedContinuousTensorSpec,
 )
-from torchrl.envs import EnvBase
 from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
 
+from lerobot.common.envs.abstract import AbstractEnv
 from lerobot.common.utils import set_seed
 
 _has_gym = importlib.util.find_spec("gym") is not None
 
 
-class PushtEnv(EnvBase):
+class PushtEnv(AbstractEnv):
     def __init__(
         self,
+        task="pusht",
         frame_skip: int = 1,
         from_pixels: bool = False,
         pixels_only: bool = False,
         image_size=None,
         seed=1337,
         device="cpu",
-        num_prev_obs=0,
+        num_prev_obs=1,
         num_prev_action=0,
     ):
-        super().__init__(device=device, batch_size=[])
-        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
-
-        if pixels_only:
-            assert from_pixels
-        if from_pixels:
-            assert image_size
+        super().__init__(
+            task=task,
+            frame_skip=frame_skip,
+            from_pixels=from_pixels,
+            pixels_only=pixels_only,
+            image_size=image_size,
+            seed=seed,
+            device=device,
+            num_prev_obs=num_prev_obs,
+            num_prev_action=num_prev_action,
+        )
 
+    def _make_env(self):
         if not _has_gym:
             raise ImportError("Cannot import gym.")
 
         # TODO(rcadene) (PushTEnv is similar to PushTImageEnv, but without the image rendering, it's faster to iterate on)
         # from lerobot.common.envs.pusht.pusht_env import PushTEnv
 
-        if not from_pixels:
+        if not self.from_pixels:
             raise NotImplementedError("Use PushTEnv, instead of PushTImageEnv")
         from lerobot.common.envs.pusht.pusht_image_env import PushTImageEnv
 
         self._env = PushTImageEnv(render_size=self.image_size)
 
-        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 render(self, mode="rgb_array", width=384, height=384):
         if width != height:
             raise NotImplementedError()
@@ -122,6 +113,8 @@ class PushtEnv(EnvBase):
             )
         else:
             raise NotImplementedError()
+
+        self.call_rendering_hooks()
         return td
 
     def _step(self, tensordict: TensorDict):
@@ -154,6 +147,8 @@ class PushtEnv(EnvBase):
                     stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
                 obs = stacked_obs
 
+            self.call_rendering_hooks()
+
         td = TensorDict(
             {
                 "observation": TensorDict(obs, batch_size=[]),
@@ -175,9 +170,9 @@ class PushtEnv(EnvBase):
 
             obs["image"] = BoundedTensorSpec(
                 low=0,
-                high=1,
+                high=255,
                 shape=image_shape,
-                dtype=torch.float32,
+                dtype=torch.uint8,
                 device=self.device,
             )
             if not self.pixels_only: