Add Aloha env and ACT policy

.github/workflows/test.yml

@@ -18,6 +18,12 @@ jobs:
     env:
       POETRY_VERSION: 1.8.1
       DATA_DIR: tests/data
+      TMPDIR: ~/tmp
+      TEMP: ~/tmp
+      TMP: ~/tmp
+      PYOPENGL_PLATFORM: egl
+      MUJOCO_GL: egl
+      LEROBOT_TESTS_DEVICE: cpu
     steps:
       #----------------------------------------------
       #       check-out repo and set-up python
@@ -26,11 +32,13 @@ jobs:
         uses: actions/checkout@v4
         with:
           lfs: true
+
       - name: Set up python
         id: setup-python
         uses: actions/setup-python@v5
         with:
           python-version: '3.10'
+
       #----------------------------------------------
       #         install & configure poetry
       #----------------------------------------------
@@ -40,13 +48,15 @@ jobs:
         with:
           path: ~/.local  # the path depends on the OS
           key: poetry-${{ env.POETRY_VERSION }}  # increment to reset cache
+
       - name: Install Poetry
         if: steps.restore-poetry-cache.outputs.cache-hit != 'true'
         uses: snok/install-poetry@v1
         with:
           version: ${{ env.POETRY_VERSION }}
           virtualenvs-create: true
           installer-parallel: true
+
       - name: Save cached Poetry installation
         if: |
           steps.restore-poetry-cache.outputs.cache-hit != 'true' &&
@@ -56,8 +66,10 @@ jobs:
         with:
           path: ~/.local  # the path depends on the OS
           key: poetry-${{ env.POETRY_VERSION }}  # increment to reset cache
+
       - name: Configure Poetry
         run: poetry config virtualenvs.in-project true
+
       #----------------------------------------------
       #           install dependencies
       #----------------------------------------------
@@ -67,9 +79,21 @@ jobs:
         with:
           path: .venv
           key: venv-${{ steps.setup-python.outputs.python-version }}-${{ env.POETRY_VERSION }}-${{ hashFiles('**/poetry.lock') }}
+
+      - name: Info
+        run: |
+          sudo du -sh /tmp
+          sudo df -h
+
       - name: Install dependencies
         if: steps.restore-dependencies-cache.outputs.cache-hit != 'true'
-        run: poetry install --no-interaction --no-root
+        run: |
+          mkdir ~/tmp
+          echo $TMPDIR
+          echo $TEMP
+          echo $TMP
+          poetry install --no-interaction --no-root
+
       - name: Save cached venv
         if: |
             steps.restore-dependencies-cache.outputs.cache-hit != 'true' &&
@@ -79,18 +103,24 @@ jobs:
         with:
           path: .venv
           key: venv-${{ steps.setup-python.outputs.python-version }}-${{ env.POETRY_VERSION }}-${{ hashFiles('**/poetry.lock') }}
+
+      - name: Install libegl1-mesa-dev (to use MUJOCO_GL=egl)
+        run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
+
       #----------------------------------------------
       #             install project
       #----------------------------------------------
       - name: Install project
         run: poetry install --no-interaction
+
       #----------------------------------------------
       #               run tests
       #----------------------------------------------
       - name: Run tests
         run: |
           source .venv/bin/activate
           pytest tests
+
       - name: Test train pusht end-to-end
         run: |
           source .venv/bin/activate
@@ -104,6 +134,7 @@ jobs:
             save_model=true \
             save_freq=1 \
             hydra.run.dir=tests/outputs/
+
       - name: Test eval pusht end-to-end
         run: |
           source .venv/bin/activate

README.md

@@ -59,19 +59,10 @@ env=pusht
 
 ## TODO
 
-- [x] priority update doesn't match FOWM or original paper
-- [x] self.step=100000 should be updated at every step to adjust to the horizon of the planner
-- [ ] prefetch replay buffer to speedup training
-- [ ] parallelize env to speed up eval
-- [ ] clean checkpointing / loading
-- [ ] clean logging
-- [ ] clean config
-- [ ] clean hyperparameter tuning
-- [ ] add pusht
-- [ ] add aloha
-- [ ] add act
-- [ ] add diffusion
-- [ ] add aloha 2
+If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/users/Cadene/projects/1)
+
+Ask [Remi Cadene](re.cadene@gmail.com) for access if needed.
+
 
 ## Profile
 

lerobot/common/datasets/abstract.py

@@ -81,7 +81,10 @@ def transform(self):
     def set_transform(self, transform):
         if not isinstance(transform, Compose):
             # required since torchrl calls `len(self._transform)` downstream
-            self._transform = Compose(transform)
+            if isinstance(transform, list):
+                self._transform = Compose(*transform)
+            else:
+                self._transform = Compose(transform)
         else:
             self._transform = transform
 

lerobot/common/datasets/aloha.py

@@ -73,11 +73,11 @@ def download(data_dir, dataset_id):
 
     data_dir.mkdir(parents=True, exist_ok=True)
 
-    gdown.download_folder(FOLDER_URLS[dataset_id], output=data_dir)
+    gdown.download_folder(FOLDER_URLS[dataset_id], output=str(data_dir))
 
     # because of the 50 files limit per directory, two files episode 48 and 49 were missing
-    gdown.download(EP48_URLS[dataset_id], output=data_dir / "episode_48.hdf5", fuzzy=True)
-    gdown.download(EP49_URLS[dataset_id], output=data_dir / "episode_49.hdf5", fuzzy=True)
+    gdown.download(EP48_URLS[dataset_id], output=str(data_dir / "episode_48.hdf5"), fuzzy=True)
+    gdown.download(EP49_URLS[dataset_id], output=str(data_dir / "episode_49.hdf5"), fuzzy=True)
 
 
 class AlohaExperienceReplay(AbstractExperienceReplay):
@@ -124,9 +124,6 @@ def stats_patterns(self) -> dict:
     def image_keys(self) -> list:
         return [("observation", "image", cam) for cam in CAMERAS[self.dataset_id]]
 
-    # def _is_downloaded(self) -> bool:
-    #     return False
-
     def _download_and_preproc(self):
         raw_dir = self.data_dir.parent / f"{self.data_dir.name}_raw"
         if not raw_dir.is_dir():

lerobot/common/datasets/factory.py

@@ -5,7 +5,7 @@
 import torch
 from torchrl.data.replay_buffers import PrioritizedSliceSampler, SliceSampler
 
-from lerobot.common.envs.transforms import NormalizeTransform
+from lerobot.common.envs.transforms import NormalizeTransform, Prod
 
 DATA_DIR = Path(os.environ.get("DATA_DIR", "data"))
 
@@ -84,6 +84,16 @@ def make_offline_buffer(
         prefetch=prefetch if isinstance(prefetch, int) else None,
     )
 
+    if cfg.policy.name == "tdmpc":
+        img_keys = []
+        for key in offline_buffer.image_keys:
+            img_keys.append(("next", *key))
+        img_keys += offline_buffer.image_keys
+    else:
+        img_keys = offline_buffer.image_keys
+
+    transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
+
     if normalize:
         # TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max, min_max_from_spec
         stats = offline_buffer.compute_or_load_stats()
@@ -92,11 +102,10 @@ def make_offline_buffer(
         in_keys = [("observation", "state"), ("action")]
 
         if cfg.policy.name == "tdmpc":
-            for key in offline_buffer.image_keys:
-                # TODO(rcadene): imagenet normalization is applied inside diffusion policy, but no normalization inside tdmpc
-                in_keys.append(key)
-                # since we use next observations in tdmpc
-                in_keys.append(("next", *key))
+            # TODO(rcadene): we add img_keys to the keys to normalize for tdmpc only, since diffusion and act policies normalize the image inside the model for now
+            in_keys += img_keys
+            # TODO(racdene): since we use next observations in tdmpc, we also add them to the normalization. We are wasting a bit of compute on this for now.
+            in_keys += [("next", *key) for key in img_keys]
             in_keys.append(("next", "observation", "state"))
 
         if cfg.policy.name == "diffusion" and cfg.env.name == "pusht":
@@ -106,8 +115,11 @@ def make_offline_buffer(
             stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
             stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
 
-        transform = NormalizeTransform(stats, in_keys, mode="min_max")
-        offline_buffer.set_transform(transform)
+        # TODO(rcadene): remove this and put it in config. Ideally we want to reproduce SOTA results just with mean_std
+        normalization_mode = "mean_std" if cfg.env.name == "aloha" else "min_max"
+        transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode))
+
+    offline_buffer.set_transform(transforms)
 
     if not overwrite_sampler:
         index = torch.arange(0, offline_buffer.num_samples, 1)

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 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 @@ def __init__(
         )
 
     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}'")

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()