Merge pull request #47 from huggingface/user/aliberts/2024_03_22_fix_simxarm
Port simxarm, upgrade gym to gymnasium
This commit is contained in:
Simon Alibert
GitHub
commit
203bcd7ca5
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@ -1 +1,2 @@
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*.memmap filter=lfs diff=lfs merge=lfs -text
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*.stl filter=lfs diff=lfs merge=lfs -text
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@ -338,73 +338,6 @@ files = [
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[[package]]
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name = "debugpy"
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@ -500,13 +433,13 @@ files = [
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[[package]]
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name = "dm-control"
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version = "1.0.16"
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version = "1.0.14"
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description = "Continuous control environments and MuJoCo Python bindings."
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python-versions = ">=3.8"
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files = [
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{file = "dm_control-1.0.16-py3-none-any.whl", hash = "sha256:341f582e26d88556ac0dd1963e11c25f93ef6649b07c75d04570a7de3edebd1b"},
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[package.dependencies]
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@ -516,7 +449,7 @@ dm-tree = "!=0.1.2"
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glfw = "*"
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labmaze = "*"
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lxml = "*"
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mujoco = ">=3.1.1"
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mujoco = ">=2.3.7"
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numpy = ">=1.9.0"
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protobuf = ">=3.19.4"
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pyopengl = ">=3.1.4"
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@ -635,43 +568,6 @@ files = [
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[[package]]
|
||||
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|
||||
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|
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description = "Collection of common python utils"
|
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|
||||
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||||
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||||
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|
||||
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[package.dependencies]
|
||||
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|
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importlib_resources = {version = "*", optional = true, markers = "extra == \"epath\""}
|
||||
typing_extensions = {version = "*", optional = true, markers = "extra == \"epy\""}
|
||||
zipp = {version = "*", optional = true, markers = "extra == \"epath\""}
|
||||
|
||||
[package.extras]
|
||||
all = ["etils[array-types]", "etils[eapp]", "etils[ecolab]", "etils[edc]", "etils[enp]", "etils[epath-gcs]", "etils[epath-s3]", "etils[epath]", "etils[epy]", "etils[etqdm]", "etils[etree-dm]", "etils[etree-jax]", "etils[etree-tf]", "etils[etree]"]
|
||||
array-types = ["etils[enp]"]
|
||||
dev = ["chex", "dataclass_array", "optree", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-subtests", "pytest-xdist", "torch"]
|
||||
docs = ["etils[all,dev]", "sphinx-apitree[ext]"]
|
||||
eapp = ["absl-py", "etils[epy]", "simple_parsing"]
|
||||
ecolab = ["etils[enp]", "etils[epy]", "etils[etree]", "jupyter", "mediapy", "numpy", "packaging", "protobuf"]
|
||||
edc = ["etils[epy]"]
|
||||
enp = ["etils[epy]", "numpy"]
|
||||
epath = ["etils[epy]", "fsspec", "importlib_resources", "typing_extensions", "zipp"]
|
||||
epath-gcs = ["etils[epath]", "gcsfs"]
|
||||
epath-s3 = ["etils[epath]", "s3fs"]
|
||||
epy = ["typing_extensions"]
|
||||
etqdm = ["absl-py", "etils[epy]", "tqdm"]
|
||||
etree = ["etils[array-types]", "etils[enp]", "etils[epy]", "etils[etqdm]"]
|
||||
etree-dm = ["dm-tree", "etils[etree]"]
|
||||
etree-jax = ["etils[etree]", "jax[cpu]"]
|
||||
etree-tf = ["etils[etree]", "tensorflow"]
|
||||
lazy-imports = ["etils[ecolab]"]
|
||||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
version = "1.2.0"
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||||
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@ -686,6 +582,17 @@ files = [
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[package.extras]
|
||||
test = ["pytest (>=6)"]
|
||||
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||||
[[package]]
|
||||
name = "farama-notifications"
|
||||
version = "0.0.4"
|
||||
description = "Notifications for all Farama Foundation maintained libraries."
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
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|
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|
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[[package]]
|
||||
name = "fasteners"
|
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version = "0.19"
|
||||
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@ -887,43 +794,58 @@ files = [
|
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protobuf = ["grpcio-tools (>=1.62.1)"]
|
||||
|
||||
[[package]]
|
||||
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|
||||
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||||
description = "Gym: A universal API for reinforcement learning environments"
|
||||
name = "gymnasium"
|
||||
version = "0.29.1"
|
||||
description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
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||||
optional = false
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||||
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|
||||
python-versions = ">=3.8"
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||||
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||||
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||||
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|
||||
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||||
farama-notifications = ">=0.0.1"
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
|
||||
[[package]]
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||||
name = "gym-notices"
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||||
version = "0.0.8"
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||||
description = "Notices for gym"
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||||
name = "gymnasium-robotics"
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||||
version = "1.2.4"
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||||
description = "Robotics environments for the Gymnasium repo."
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||||
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||||
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||||
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||||
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[[package]]
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|
@ -1105,21 +1027,6 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
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|||
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||||
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||||
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||||
[[package]]
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||||
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||||
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|
|
@ -1457,65 +1364,44 @@ tests = ["pytest (>=4.6)"]
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||||
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||||
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|
||||
|
||||
[package.dependencies]
|
||||
absl-py = "*"
|
||||
etils = {version = "*", extras = ["epath"]}
|
||||
glfw = "*"
|
||||
numpy = "*"
|
||||
pyopengl = "*"
|
||||
|
||||
[[package]]
|
||||
name = "mujoco-py"
|
||||
version = "2.1.2.14"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "mujoco-py-2.1.2.14.tar.gz", hash = "sha256:eb5b14485acf80a3cf8c15f4b080c6a28a9f79e68869aa696d16cbd51ea7706f"},
|
||||
{file = "mujoco_py-2.1.2.14-py3-none-any.whl", hash = "sha256:37c0b41bc0153a8a0eb3663103a67c60f65467753f74e4ff6e68b879f3e3a71f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cffi = ">=1.10"
|
||||
Cython = ">=0.27.2"
|
||||
fasteners = ">=0.15,<1.0"
|
||||
glfw = ">=1.4.0"
|
||||
imageio = ">=2.1.2"
|
||||
numpy = ">=1.11"
|
||||
|
||||
[[package]]
|
||||
name = "networkx"
|
||||
version = "3.2.1"
|
||||
|
|
@ -1790,6 +1676,31 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
|
|||
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
|
||||
xml = ["lxml (>=4.9.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "pettingzoo"
|
||||
version = "1.24.3"
|
||||
description = "Gymnasium for multi-agent reinforcement learning."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pettingzoo-1.24.3-py3-none-any.whl", hash = "sha256:23ed90517d2e8a7098bdaf5e31234b3a7f7b73ca578d70d1ca7b9d0cb0e37982"},
|
||||
{file = "pettingzoo-1.24.3.tar.gz", hash = "sha256:91f9094f18e06fb74b98f4099cd22e8ae4396125e51719d50b30c9f1c7ab07e6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
gymnasium = ">=0.28.0"
|
||||
numpy = ">=1.21.0"
|
||||
|
||||
[package.extras]
|
||||
all = ["box2d-py (==2.3.5)", "chess (==1.9.4)", "multi-agent-ale-py (==0.1.11)", "pillow (>=8.0.1)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "rlcard (==1.0.5)", "scipy (>=1.4.1)", "shimmy[openspiel] (>=1.2.0)"]
|
||||
atari = ["multi-agent-ale-py (==0.1.11)", "pygame (==2.3.0)"]
|
||||
butterfly = ["pygame (==2.3.0)", "pymunk (==6.2.0)"]
|
||||
classic = ["chess (==1.9.4)", "pygame (==2.3.0)", "rlcard (==1.0.5)", "shimmy[openspiel] (>=1.2.0)"]
|
||||
mpe = ["pygame (==2.3.0)"]
|
||||
other = ["pillow (>=8.0.1)"]
|
||||
sisl = ["box2d-py (==2.3.5)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "scipy (>=1.4.1)"]
|
||||
testing = ["AutoROM", "pre-commit", "pynput", "pytest", "pytest-cov", "pytest-markdown-docs", "pytest-xdist"]
|
||||
|
||||
[[package]]
|
||||
name = "pillow"
|
||||
version = "10.2.0"
|
||||
|
|
@ -3305,4 +3216,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "cbd9aedcb3a24417b85124fb94db706dd6ca0a90dfb610b0aebdcd3aa2a0333c"
|
||||
content-hash = "93c406139c456780b3d309d7ed3d68ea60cc0e8893c1ee717692984e573d3404"
|
||||
|
|
|
|||
|
|
@ -21,7 +21,6 @@ packages = [{include = "lerobot"}]
|
|||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.10"
|
||||
cython = "^3.0.8"
|
||||
termcolor = "^2.4.0"
|
||||
omegaconf = "^2.3.0"
|
||||
dm-env = "^1.6"
|
||||
|
|
@ -42,17 +41,17 @@ mpmath = "^1.3.0"
|
|||
torch = {version = "^2.2.1", source = "torch-cpu"}
|
||||
tensordict = {git = "https://github.com/pytorch/tensordict"}
|
||||
torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
|
||||
mujoco = "^3.1.2"
|
||||
mujoco-py = "^2.1.2.14"
|
||||
gym = "^0.26.2"
|
||||
mujoco = "^2.3.7"
|
||||
opencv-python = "^4.9.0.80"
|
||||
diffusers = "^0.26.3"
|
||||
torchvision = {version = "^0.17.1", source = "torch-cpu"}
|
||||
h5py = "^3.10.0"
|
||||
dm = "^1.3"
|
||||
dm-control = "^1.0.16"
|
||||
dm-control = "1.0.14"
|
||||
robomimic = "0.2.0"
|
||||
huggingface-hub = "^0.21.4"
|
||||
gymnasium-robotics = "^1.2.4"
|
||||
gymnasium = "^0.29.1"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
|
|
|
|||
|
|
@ -16,12 +16,8 @@ jobs:
|
|||
${{ github.event_name == 'push' }}
|
||||
runs-on: ubuntu-latest
|
||||
env:
|
||||
POETRY_VERSION: 1.8.1
|
||||
POETRY_VERSION: 1.8.2
|
||||
DATA_DIR: tests/data
|
||||
TMPDIR: ~/tmp
|
||||
TEMP: ~/tmp
|
||||
TMP: ~/tmp
|
||||
PYOPENGL_PLATFORM: egl
|
||||
MUJOCO_GL: egl
|
||||
LEROBOT_TESTS_DEVICE: cpu
|
||||
steps:
|
||||
|
|
@ -86,6 +82,10 @@ jobs:
|
|||
|
||||
- name: Install dependencies
|
||||
if: steps.restore-dependencies-cache.outputs.cache-hit != 'true'
|
||||
env:
|
||||
TMPDIR: ~/tmp
|
||||
TEMP: ~/tmp
|
||||
TMP: ~/tmp
|
||||
run: |
|
||||
mkdir ~/tmp
|
||||
poetry install --no-interaction --no-root
|
||||
|
|
|
|||
|
|
@ -14,11 +14,11 @@ repos:
|
|||
- id: end-of-file-fixer
|
||||
- id: trailing-whitespace
|
||||
- repo: https://github.com/asottile/pyupgrade
|
||||
rev: v3.15.1
|
||||
rev: v3.15.2
|
||||
hooks:
|
||||
- id: pyupgrade
|
||||
- repo: https://github.com/astral-sh/ruff-pre-commit
|
||||
rev: v0.2.2
|
||||
rev: v0.3.4
|
||||
hooks:
|
||||
- id: ruff
|
||||
args: [--fix]
|
||||
|
|
|
|||
25
LICENSE
25
LICENSE
|
|
@ -253,6 +253,31 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|||
SOFTWARE.
|
||||
|
||||
|
||||
## Some of lerobot's code is derived from simxarm, which is subject to the following copyright notice:
|
||||
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2023 Nicklas Hansen & Yanjie Ze
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
|
||||
|
||||
## Some of lerobot's code is derived from ALOHA, which is subject to the following copyright notice:
|
||||
|
||||
MIT License
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
|
|||
def __init__(
|
||||
self,
|
||||
dataset_id: str,
|
||||
version: str | None = None,
|
||||
version: str | None = "v1.1",
|
||||
batch_size: int = None,
|
||||
*,
|
||||
shuffle: bool = True,
|
||||
|
|
@ -67,11 +67,11 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
|
|||
)
|
||||
|
||||
def _download_and_preproc_obsolete(self):
|
||||
assert self.root is not None
|
||||
# assert self.root is not None
|
||||
# TODO(rcadene): finish download
|
||||
download()
|
||||
# download()
|
||||
|
||||
dataset_path = self.root / f"{self.dataset_id}_raw" / "buffer.pkl"
|
||||
dataset_path = self.root / f"{self.dataset_id}" / "buffer.pkl"
|
||||
print(f"Using offline dataset '{dataset_path}'")
|
||||
with open(dataset_path, "rb") as f:
|
||||
dataset_dict = pickle.load(f)
|
||||
|
|
@ -105,15 +105,19 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
|
|||
"frame_id": torch.arange(0, num_frames, 1),
|
||||
("next", "observation", "image"): next_image,
|
||||
("next", "observation", "state"): next_state,
|
||||
("next", "observation", "reward"): next_reward,
|
||||
("next", "observation", "done"): next_done,
|
||||
("next", "reward"): next_reward,
|
||||
("next", "done"): next_done,
|
||||
},
|
||||
batch_size=num_frames,
|
||||
)
|
||||
|
||||
if episode_id == 0:
|
||||
# hack to initialize tensordict data structure to store episodes
|
||||
td_data = episode[0].expand(total_frames).memmap_like(self.root / f"{self.dataset_id}")
|
||||
td_data = (
|
||||
episode[0]
|
||||
.expand(total_frames)
|
||||
.memmap_like(self.root / f"{self.dataset_id}" / "replay_buffer")
|
||||
)
|
||||
|
||||
td_data[idx0:idx1] = episode
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from typing import Optional
|
|||
from tensordict import TensorDict
|
||||
from torchrl.envs import EnvBase
|
||||
|
||||
from lerobot.common.utils import set_seed
|
||||
from lerobot.common.utils import set_global_seed
|
||||
|
||||
|
||||
class AbstractEnv(EnvBase):
|
||||
|
|
@ -67,4 +67,4 @@ class AbstractEnv(EnvBase):
|
|||
raise NotImplementedError("Abstract method")
|
||||
|
||||
def _set_seed(self, seed: Optional[int]):
|
||||
set_seed(seed)
|
||||
set_global_seed(seed)
|
||||
|
|
|
|||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -29,9 +29,9 @@ from lerobot.common.envs.aloha.tasks.sim_end_effector import (
|
|||
TransferCubeEndEffectorTask,
|
||||
)
|
||||
from lerobot.common.envs.aloha.utils import sample_box_pose, sample_insertion_pose
|
||||
from lerobot.common.utils import set_seed
|
||||
from lerobot.common.utils import set_global_seed
|
||||
|
||||
_has_gym = importlib.util.find_spec("gym") is not None
|
||||
_has_gym = importlib.util.find_spec("gymnasium") is not None
|
||||
|
||||
|
||||
class AlohaEnv(AbstractEnv):
|
||||
|
|
@ -63,7 +63,7 @@ class AlohaEnv(AbstractEnv):
|
|||
|
||||
def _make_env(self):
|
||||
if not _has_gym:
|
||||
raise ImportError("Cannot import gym.")
|
||||
raise ImportError("Cannot import gymnasium.")
|
||||
|
||||
if not self.from_pixels:
|
||||
raise NotImplementedError()
|
||||
|
|
@ -290,7 +290,7 @@ class AlohaEnv(AbstractEnv):
|
|||
)
|
||||
|
||||
def _set_seed(self, seed: Optional[int]):
|
||||
set_seed(seed)
|
||||
set_global_seed(seed)
|
||||
# TODO(rcadene): seed the env
|
||||
# self._env.seed(seed)
|
||||
logging.warning("Aloha env is not seeded")
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ def make_env(cfg, transform=None):
|
|||
}
|
||||
|
||||
if cfg.env.name == "simxarm":
|
||||
from lerobot.common.envs.simxarm import SimxarmEnv
|
||||
from lerobot.common.envs.simxarm.env import SimxarmEnv
|
||||
|
||||
kwargs["task"] = cfg.env.task
|
||||
clsfunc = SimxarmEnv
|
||||
|
|
|
|||
|
|
@ -16,9 +16,9 @@ from torchrl.data.tensor_specs import (
|
|||
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
|
||||
from lerobot.common.utils import set_global_seed
|
||||
|
||||
_has_gym = importlib.util.find_spec("gym") is not None
|
||||
_has_gym = importlib.util.find_spec("gymnasium") is not None
|
||||
|
||||
|
||||
class PushtEnv(AbstractEnv):
|
||||
|
|
@ -50,7 +50,7 @@ class PushtEnv(AbstractEnv):
|
|||
|
||||
def _make_env(self):
|
||||
if not _has_gym:
|
||||
raise ImportError("Cannot import gym.")
|
||||
raise ImportError("Cannot import gymnasium.")
|
||||
|
||||
# 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
|
||||
|
|
@ -238,6 +238,6 @@ class PushtEnv(AbstractEnv):
|
|||
|
||||
def _set_seed(self, seed: Optional[int]):
|
||||
# Set global seed.
|
||||
set_seed(seed)
|
||||
set_global_seed(seed)
|
||||
# Set PushTImageEnv seed as it relies on it's own internal _seed attribute.
|
||||
self._env.seed(seed)
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
import collections
|
||||
|
||||
import cv2
|
||||
import gym
|
||||
import gymnasium as gym
|
||||
import numpy as np
|
||||
import pygame
|
||||
import pymunk
|
||||
import pymunk.pygame_util
|
||||
import shapely.geometry as sg
|
||||
import skimage.transform as st
|
||||
from gym import spaces
|
||||
from gymnasium import spaces
|
||||
from pymunk.vec2d import Vec2d
|
||||
|
||||
from lerobot.common.envs.pusht.pymunk_override import DrawOptions
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import numpy as np
|
||||
from gym import spaces
|
||||
from gymnasium import spaces
|
||||
|
||||
from lerobot.common.envs.pusht.pusht_env import PushTEnv
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import importlib
|
||||
import logging
|
||||
from collections import deque
|
||||
from typing import Optional
|
||||
|
||||
|
|
@ -15,12 +16,11 @@ from torchrl.data.tensor_specs import (
|
|||
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
|
||||
from lerobot.common.utils import set_global_seed
|
||||
|
||||
MAX_NUM_ACTIONS = 4
|
||||
|
||||
_has_gym = importlib.util.find_spec("gym") is not None
|
||||
_has_simxarm = importlib.util.find_spec("simxarm") is not None and _has_gym
|
||||
_has_gym = importlib.util.find_spec("gymnasium") is not None
|
||||
|
||||
|
||||
class SimxarmEnv(AbstractEnv):
|
||||
|
|
@ -49,13 +49,12 @@ class SimxarmEnv(AbstractEnv):
|
|||
)
|
||||
|
||||
def _make_env(self):
|
||||
if not _has_simxarm:
|
||||
raise ImportError("Cannot import simxarm.")
|
||||
if not _has_gym:
|
||||
raise ImportError("Cannot import gym.")
|
||||
raise ImportError("Cannot import gymnasium.")
|
||||
|
||||
import gym
|
||||
from simxarm import TASKS
|
||||
import gymnasium
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm import TASKS
|
||||
|
||||
if self.task not in TASKS:
|
||||
raise ValueError(f"Unknown task {self.task}. Must be one of {list(TASKS.keys())}")
|
||||
|
|
@ -63,7 +62,7 @@ class SimxarmEnv(AbstractEnv):
|
|||
self._env = TASKS[self.task]["env"]()
|
||||
|
||||
num_actions = len(TASKS[self.task]["action_space"])
|
||||
self._action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,))
|
||||
self._action_space = gymnasium.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,))
|
||||
self._action_padding = np.zeros((MAX_NUM_ACTIONS - num_actions), dtype=np.float32)
|
||||
if "w" not in TASKS[self.task]["action_space"]:
|
||||
self._action_padding[-1] = 1.0
|
||||
|
|
@ -84,7 +83,7 @@ class SimxarmEnv(AbstractEnv):
|
|||
else:
|
||||
obs = {"state": torch.tensor(raw_obs["observation"], dtype=torch.float32)}
|
||||
|
||||
obs = TensorDict(obs, batch_size=[])
|
||||
# obs = TensorDict(obs, batch_size=[])
|
||||
return obs
|
||||
|
||||
def _reset(self, tensordict: Optional[TensorDict] = None):
|
||||
|
|
@ -229,5 +228,7 @@ class SimxarmEnv(AbstractEnv):
|
|||
)
|
||||
|
||||
def _set_seed(self, seed: Optional[int]):
|
||||
set_seed(seed)
|
||||
self._env.seed(seed)
|
||||
set_global_seed(seed)
|
||||
self._seed = seed
|
||||
# TODO(aliberts): change self._reset so that it takes in a seed value
|
||||
logging.warning("simxarm env is not properly seeded")
|
||||
|
|
@ -0,0 +1,166 @@
|
|||
from collections import OrderedDict, deque
|
||||
|
||||
import gymnasium as gym
|
||||
import numpy as np
|
||||
from gymnasium.wrappers import TimeLimit
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm.tasks.base import Base as Base
|
||||
from lerobot.common.envs.simxarm.simxarm.tasks.lift import Lift
|
||||
from lerobot.common.envs.simxarm.simxarm.tasks.peg_in_box import PegInBox
|
||||
from lerobot.common.envs.simxarm.simxarm.tasks.push import Push
|
||||
from lerobot.common.envs.simxarm.simxarm.tasks.reach import Reach
|
||||
|
||||
TASKS = OrderedDict(
|
||||
(
|
||||
(
|
||||
"reach",
|
||||
{
|
||||
"env": Reach,
|
||||
"action_space": "xyz",
|
||||
"episode_length": 50,
|
||||
"description": "Reach a target location with the end effector",
|
||||
},
|
||||
),
|
||||
(
|
||||
"push",
|
||||
{
|
||||
"env": Push,
|
||||
"action_space": "xyz",
|
||||
"episode_length": 50,
|
||||
"description": "Push a cube to a target location",
|
||||
},
|
||||
),
|
||||
(
|
||||
"peg_in_box",
|
||||
{
|
||||
"env": PegInBox,
|
||||
"action_space": "xyz",
|
||||
"episode_length": 50,
|
||||
"description": "Insert a peg into a box",
|
||||
},
|
||||
),
|
||||
(
|
||||
"lift",
|
||||
{
|
||||
"env": Lift,
|
||||
"action_space": "xyzw",
|
||||
"episode_length": 50,
|
||||
"description": "Lift a cube above a height threshold",
|
||||
},
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
class SimXarmWrapper(gym.Wrapper):
|
||||
"""
|
||||
A wrapper for the SimXarm environments. This wrapper is used to
|
||||
convert the action and observation spaces to the correct format.
|
||||
"""
|
||||
|
||||
def __init__(self, env, task, obs_mode, image_size, action_repeat, frame_stack=1, channel_last=False):
|
||||
super().__init__(env)
|
||||
self._env = env
|
||||
self.obs_mode = obs_mode
|
||||
self.image_size = image_size
|
||||
self.action_repeat = action_repeat
|
||||
self.frame_stack = frame_stack
|
||||
self._frames = deque([], maxlen=frame_stack)
|
||||
self.channel_last = channel_last
|
||||
self._max_episode_steps = task["episode_length"] // action_repeat
|
||||
|
||||
image_shape = (
|
||||
(image_size, image_size, 3 * frame_stack)
|
||||
if channel_last
|
||||
else (3 * frame_stack, image_size, image_size)
|
||||
)
|
||||
if obs_mode == "state":
|
||||
self.observation_space = env.observation_space["observation"]
|
||||
elif obs_mode == "rgb":
|
||||
self.observation_space = gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8)
|
||||
elif obs_mode == "all":
|
||||
self.observation_space = gym.spaces.Dict(
|
||||
state=gym.spaces.Box(low=-np.inf, high=np.inf, shape=(4,), dtype=np.float32),
|
||||
rgb=gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8),
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unknown obs_mode {obs_mode}. Must be one of [rgb, all, state]")
|
||||
self.action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(len(task["action_space"]),))
|
||||
self.action_padding = np.zeros(4 - len(task["action_space"]), dtype=np.float32)
|
||||
if "w" not in task["action_space"]:
|
||||
self.action_padding[-1] = 1.0
|
||||
|
||||
def _render_obs(self):
|
||||
obs = self.render(mode="rgb_array", width=self.image_size, height=self.image_size)
|
||||
if not self.channel_last:
|
||||
obs = obs.transpose(2, 0, 1)
|
||||
return obs.copy()
|
||||
|
||||
def _update_frames(self, reset=False):
|
||||
pixels = self._render_obs()
|
||||
self._frames.append(pixels)
|
||||
if reset:
|
||||
for _ in range(1, self.frame_stack):
|
||||
self._frames.append(pixels)
|
||||
assert len(self._frames) == self.frame_stack
|
||||
|
||||
def transform_obs(self, obs, reset=False):
|
||||
if self.obs_mode == "state":
|
||||
return obs["observation"]
|
||||
elif self.obs_mode == "rgb":
|
||||
self._update_frames(reset=reset)
|
||||
rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
|
||||
return rgb_obs
|
||||
elif self.obs_mode == "all":
|
||||
self._update_frames(reset=reset)
|
||||
rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
|
||||
return OrderedDict((("rgb", rgb_obs), ("state", self.robot_state)))
|
||||
else:
|
||||
raise ValueError(f"Unknown obs_mode {self.obs_mode}. Must be one of [rgb, all, state]")
|
||||
|
||||
def reset(self):
|
||||
return self.transform_obs(self._env.reset(), reset=True)
|
||||
|
||||
def step(self, action):
|
||||
action = np.concatenate([action, self.action_padding])
|
||||
reward = 0.0
|
||||
for _ in range(self.action_repeat):
|
||||
obs, r, done, info = self._env.step(action)
|
||||
reward += r
|
||||
return self.transform_obs(obs), reward, done, info
|
||||
|
||||
def render(self, mode="rgb_array", width=384, height=384, **kwargs):
|
||||
return self._env.render(mode, width=width, height=height)
|
||||
|
||||
@property
|
||||
def state(self):
|
||||
return self._env.robot_state
|
||||
|
||||
|
||||
def make(task, obs_mode="state", image_size=84, action_repeat=1, frame_stack=1, channel_last=False, seed=0):
|
||||
"""
|
||||
Create a new environment.
|
||||
Args:
|
||||
task (str): The task to create an environment for. Must be one of:
|
||||
- 'reach'
|
||||
- 'push'
|
||||
- 'peg-in-box'
|
||||
- 'lift'
|
||||
obs_mode (str): The observation mode to use. Must be one of:
|
||||
- 'state': Only state observations
|
||||
- 'rgb': RGB images
|
||||
- 'all': RGB images and state observations
|
||||
image_size (int): The size of the image observations
|
||||
action_repeat (int): The number of times to repeat the action
|
||||
seed (int): The random seed to use
|
||||
Returns:
|
||||
gym.Env: The environment
|
||||
"""
|
||||
if task not in TASKS:
|
||||
raise ValueError(f"Unknown task {task}. Must be one of {list(TASKS.keys())}")
|
||||
env = TASKS[task]["env"]()
|
||||
env = TimeLimit(env, TASKS[task]["episode_length"])
|
||||
env = SimXarmWrapper(env, TASKS[task], obs_mode, image_size, action_repeat, frame_stack, channel_last)
|
||||
env.seed(seed)
|
||||
|
||||
return env
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
|
||||
<mujoco>
|
||||
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
|
||||
<size nconmax="2000" njmax="500"/>
|
||||
|
||||
<option timestep="0.002">
|
||||
<flag warmstart="enable"></flag>
|
||||
</option>
|
||||
|
||||
<include file="shared.xml"></include>
|
||||
|
||||
<worldbody>
|
||||
<body name="floor0" pos="0 0 0">
|
||||
<geom name="floorgeom0" pos="1.2 -2.0 0" size="20.0 20.0 1" type="plane" condim="3" material="floor_mat"></geom>
|
||||
</body>
|
||||
|
||||
<include file="xarm.xml"></include>
|
||||
|
||||
<body pos="0.75 0 0.6325" name="pedestal0">
|
||||
<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
|
||||
<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
|
||||
</body>
|
||||
|
||||
<body pos="1.5 0.075 0.3425" name="table0">
|
||||
<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 1 1"></geom>
|
||||
</body>
|
||||
|
||||
<body name="object" pos="1.405 0.3 0.58625">
|
||||
<joint name="object_joint0" type="free" limited="false"></joint>
|
||||
<geom size="0.035 0.035 0.035" type="box" name="object0" material="block_mat" density="50000" condim="4" friction="1 1 1" solimp="1 1 1" solref="0.02 1"></geom>
|
||||
<site name="object_site" pos="0 0 0" size="0.035 0.035 0.035" rgba="1 0 0 0" type="box"></site>
|
||||
</body>
|
||||
|
||||
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
|
||||
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
|
||||
<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
|
||||
<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
|
||||
|
||||
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
|
||||
</worldbody>
|
||||
|
||||
<equality>
|
||||
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
|
||||
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
|
||||
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
|
||||
</equality>
|
||||
|
||||
<actuator>
|
||||
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
|
||||
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
|
||||
</actuator>
|
||||
</mujoco>
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:21fb81ae7fba19e3c6b2d2ca60c8051712ba273357287eb5a397d92d61c7a736
|
||||
size 1211434
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:be68ce180d11630a667a5f37f4dffcc3feebe4217d4bb3912c813b6d9ca3ec66
|
||||
size 3284
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2c6448552bf6b1c4f17334d686a5320ce051bcdfe31431edf69303d8a570d1de
|
||||
size 3284
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:748b9e197e6521914f18d1f6383a36f211136b3f33f2ad2a8c11b9f921c2cf86
|
||||
size 6284
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a44756eb72f9c214cb37e61dc209cd7073fdff3e4271a7423476ef6fd090d2d4
|
||||
size 242684
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:e8e48692ad26837bb3d6a97582c89784d09948fc09bfe4e5a59017859ff04dac
|
||||
size 366284
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:501665812b08d67e764390db781e839adc6896a9540301d60adf606f57648921
|
||||
size 22284
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:34b541122df84d2ef5fcb91b715eb19659dc15ad8d44a191dde481f780265636
|
||||
size 184184
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:61e641cd47c169ecef779683332e00e4914db729bf02dfb61bfbe69351827455
|
||||
size 225584
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e2798e7946dd70046c95455d5ba96392d0b54a6069caba91dc4ca66e1379b42
|
||||
size 237084
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c757fee95f873191a0633c355c07a360032960771cabbd7593a6cdb0f1ffb089
|
||||
size 243684
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:715ad5787c5dab57589937fd47289882707b5e1eb997e340d567785b02f4ec90
|
||||
size 229084
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:85b320aa420497827223d16d492bba8de091173374e361396fc7a5dad7bdb0cb
|
||||
size 399384
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:97115d848fbf802cb770cd9be639ae2af993103b9d9bbb0c50c943c738a36f18
|
||||
size 231684
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f6fcbc18258090eb56c21cfb17baa5ae43abc98b1958cd366f3a73b9898fc7f0
|
||||
size 2106184
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c5dee87c7f37baf554b8456ebfe0b3e8ed0b22b8938bd1add6505c2ad6d32c7d
|
||||
size 242684
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b41dd2c2c550281bf78d7cc6fa117b14786700e5c453560a0cb5fd6dfa0ffb3e
|
||||
size 366284
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:75ca1107d0a42a0f03802a9a49cab48419b31851ee8935f8f1ca06be1c1c91e8
|
||||
size 22284
|
||||
|
|
@ -0,0 +1,74 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
|
||||
<mujoco>
|
||||
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
|
||||
<size nconmax="2000" njmax="500"/>
|
||||
|
||||
<option timestep="0.001">
|
||||
<flag warmstart="enable"></flag>
|
||||
</option>
|
||||
|
||||
<include file="shared.xml"></include>
|
||||
|
||||
<worldbody>
|
||||
<body name="floor0" pos="0 0 0">
|
||||
<geom name="floorgeom0" pos="1.2 -2.0 0" size="1.0 10.0 1" type="plane" condim="3" material="floor_mat"></geom>
|
||||
</body>
|
||||
|
||||
<include file="xarm.xml"></include>
|
||||
|
||||
<body pos="0.75 0 0.6325" name="pedestal0">
|
||||
<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
|
||||
<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
|
||||
</body>
|
||||
|
||||
<body pos="1.5 0.075 0.3425" name="table0">
|
||||
<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 0.005 0.0002"></geom>
|
||||
</body>
|
||||
|
||||
<body name="box0" pos="1.605 0.25 0.55">
|
||||
<joint name="box_joint0" type="free" limited="false"></joint>
|
||||
<site name="box_site" pos="0 0.075 -0.01" size="0.02" rgba="0 0 0 0" type="sphere"></site>
|
||||
<geom name="box_side0" pos="0 0 0" size="0.065 0.002 0.04" type= "box" rgba="0.8 0.1 0.1 1" mass ="1" condim="4" />
|
||||
<geom name="box_side1" pos="0 0.149 0" size="0.065 0.002 0.04" type="box" rgba="0.9 0.2 0.2 1" mass ="2" condim="4" />
|
||||
<geom name="box_side2" pos="0.064 0.074 0" size="0.002 0.075 0.04" type="box" rgba="0.8 0.1 0.1 1" mass ="2" condim="4" />
|
||||
<geom name="box_side3" pos="-0.064 0.074 0" size="0.002 0.075 0.04" type="box" rgba="0.9 0.2 0.2 1" mass ="2" condim="4" />
|
||||
<geom name="box_side4" pos="-0 0.074 -0.038" size="0.065 0.075 0.002" type="box" rgba="0.5 0 0 1" mass ="2" condim="4"/>
|
||||
</body>
|
||||
|
||||
<body name="object0" pos="1.4 0.25 0.65">
|
||||
<joint name="object_joint0" type="free" limited="false"></joint>
|
||||
<geom name="object_target0" type="cylinder" pos="0 0 -0.05" size="0.03 0.035" rgba="0.6 0.8 0.5 1" mass ="0.1" condim="3" />
|
||||
<site name="object_site" pos="0 0 -0.05" size="0.0325 0.0375" rgba="0 0 0 0" type="cylinder"></site>
|
||||
<body name="B0" pos="0 0 0" euler="0 0 0 ">
|
||||
<joint name="B0:joint" type="slide" limited="true" axis="0 0 1" damping="0.05" range="0.0001 0.0001001" solimpfriction="0.98 0.98 0.95" frictionloss="1"></joint>
|
||||
<geom type="capsule" size="0.002 0.03" rgba="0 0 0 1" mass="0.001" condim="4"/>
|
||||
<body name="B1" pos="0 0 0.04" euler="0 3.14 0 ">
|
||||
<joint name="B1:joint1" type="hinge" axis="1 0 0" range="-0.1 0.1" frictionloss="1"></joint>
|
||||
<joint name="B1:joint2" type="hinge" axis="0 1 0" range="-0.1 0.1" frictionloss="1"></joint>
|
||||
<joint name="B1:joint3" type="hinge" axis="0 0 1" range="-0.1 0.1" frictionloss="1"></joint>
|
||||
<geom type="capsule" size="0.002 0.004" rgba="1 0 0 0" mass="0.001" condim="4"/>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
|
||||
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
|
||||
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
|
||||
<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
|
||||
<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
|
||||
|
||||
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
|
||||
</worldbody>
|
||||
|
||||
<equality>
|
||||
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
|
||||
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
|
||||
<weld body1="right_hand" body2="B1" solimp="0.99 0.99 0.99" solref="0.02 1"></weld>
|
||||
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
|
||||
</equality>
|
||||
|
||||
<actuator>
|
||||
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
|
||||
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
|
||||
</actuator>
|
||||
</mujoco>
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
|
||||
<mujoco>
|
||||
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
|
||||
<size nconmax="2000" njmax="500"/>
|
||||
|
||||
<option timestep="0.002">
|
||||
<flag warmstart="enable"></flag>
|
||||
</option>
|
||||
|
||||
<include file="shared.xml"></include>
|
||||
|
||||
<worldbody>
|
||||
<body name="floor0" pos="0 0 0">
|
||||
<geom name="floorgeom0" pos="1.2 -2.0 0" size="1.0 10.0 1" type="plane" condim="3" material="floor_mat"></geom>
|
||||
<site name="target0" pos="1.565 0.3 0.545" size="0.0475 0.001" rgba="1 0 0 1" type="cylinder"></site>
|
||||
</body>
|
||||
|
||||
<include file="xarm.xml"></include>
|
||||
|
||||
<body pos="0.75 0 0.6325" name="pedestal0">
|
||||
<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
|
||||
<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
|
||||
</body>
|
||||
|
||||
<body pos="1.5 0.075 0.3425" name="table0">
|
||||
<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 0.005 0.0002"></geom>
|
||||
</body>
|
||||
|
||||
<body name="object" pos="1.655 0.3 0.68">
|
||||
<joint name="object_joint0" type="free" limited="false"></joint>
|
||||
<geom size="0.024 0.024 0.024" type="box" name="object" material="block_mat" density="50000" condim="4" friction="1 1 1" solimp="1 1 1" solref="0.02 1"></geom>
|
||||
<site name="object_site" pos="0 0 0" size="0.024 0.024 0.024" rgba="0 0 0 0" type="box"></site>
|
||||
</body>
|
||||
|
||||
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
|
||||
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
|
||||
<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
|
||||
<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
|
||||
|
||||
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
|
||||
</worldbody>
|
||||
|
||||
<equality>
|
||||
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
|
||||
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
|
||||
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
|
||||
</equality>
|
||||
|
||||
<actuator>
|
||||
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
|
||||
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
|
||||
</actuator>
|
||||
</mujoco>
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
|
||||
<mujoco>
|
||||
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
|
||||
<size nconmax="2000" njmax="500"/>
|
||||
|
||||
<option timestep="0.002">
|
||||
<flag warmstart="enable"></flag>
|
||||
</option>
|
||||
|
||||
<include file="shared.xml"></include>
|
||||
|
||||
<worldbody>
|
||||
<body name="floor0" pos="0 0 0">
|
||||
<geom name="floorgeom0" pos="1.2 -2.0 0" size="1.0 10.0 1" type="plane" condim="3" material="floor_mat"></geom>
|
||||
<site name="target0" pos="1.605 0.3 0.58" size="0.0475 0.001" rgba="1 0 0 1" type="cylinder"></site>
|
||||
</body>
|
||||
|
||||
<include file="xarm.xml"></include>
|
||||
|
||||
<body pos="0.75 0 0.6325" name="pedestal0">
|
||||
<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
|
||||
<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
|
||||
</body>
|
||||
|
||||
<body pos="1.5 0.075 0.3425" name="table0">
|
||||
<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 0.005 0.0002"></geom>
|
||||
</body>
|
||||
|
||||
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
|
||||
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
|
||||
<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
|
||||
<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
|
||||
|
||||
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
|
||||
</worldbody>
|
||||
|
||||
<equality>
|
||||
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
|
||||
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
|
||||
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
|
||||
</equality>
|
||||
|
||||
<actuator>
|
||||
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
|
||||
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
|
||||
</actuator>
|
||||
</mujoco>
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
<mujoco>
|
||||
<asset>
|
||||
<texture type="skybox" builtin="gradient" rgb1="0.0 0.0 0.0" rgb2="0.0 0.0 0.0" width="32" height="32"></texture>
|
||||
<material name="floor_mat" specular="0" shininess="0.0" reflectance="0" rgba="0.043 0.055 0.051 1"></material>
|
||||
|
||||
<material name="table_mat" specular="0.2" shininess="0.2" reflectance="0" rgba="1 1 1 1"></material>
|
||||
<material name="pedestal_mat" specular="0.35" shininess="0.5" reflectance="0" rgba="0.705 0.585 0.405 1"></material>
|
||||
<material name="block_mat" specular="0.5" shininess="0.9" reflectance="0.05" rgba="0.373 0.678 0.627 1"></material>
|
||||
|
||||
<material name="robot0:geomMat" shininess="0.03" specular="0.4"></material>
|
||||
<material name="robot0:gripper_finger_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
|
||||
<material name="robot0:gripper_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
|
||||
<material name="background:gripper_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
|
||||
<material name="robot0:arm_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
|
||||
<material name="robot0:head_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
|
||||
<material name="robot0:torso_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
|
||||
<material name="robot0:base_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
|
||||
|
||||
<mesh name="link_base" file="link_base.stl" />
|
||||
<mesh name="link1" file="link1.stl" />
|
||||
<mesh name="link2" file="link2.stl" />
|
||||
<mesh name="link3" file="link3.stl" />
|
||||
<mesh name="link4" file="link4.stl" />
|
||||
<mesh name="link5" file="link5.stl" />
|
||||
<mesh name="link6" file="link6.stl" />
|
||||
<mesh name="link7" file="link7.stl" />
|
||||
<mesh name="base_link" file="base_link.stl" />
|
||||
<mesh name="left_outer_knuckle" file="left_outer_knuckle.stl" />
|
||||
<mesh name="left_finger" file="left_finger.stl" />
|
||||
<mesh name="left_inner_knuckle" file="left_inner_knuckle.stl" />
|
||||
<mesh name="right_outer_knuckle" file="right_outer_knuckle.stl" />
|
||||
<mesh name="right_finger" file="right_finger.stl" />
|
||||
<mesh name="right_inner_knuckle" file="right_inner_knuckle.stl" />
|
||||
</asset>
|
||||
|
||||
<equality>
|
||||
<weld body1="robot0:mocap2" body2="link7" solimp="0.9 0.95 0.001" solref="0.02 1"></weld>
|
||||
</equality>
|
||||
|
||||
<default>
|
||||
<joint armature="1" damping="0.1" limited="true"/>
|
||||
<default class="robot0:blue">
|
||||
<geom rgba="0.086 0.506 0.767 1.0"></geom>
|
||||
</default>
|
||||
|
||||
<default class="robot0:grey">
|
||||
<geom rgba="0.356 0.361 0.376 1.0"></geom>
|
||||
</default>
|
||||
</default>
|
||||
|
||||
</mujoco>
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
<mujoco model="xarm7">
|
||||
<body mocap="true" name="robot0:mocap2" pos="0 0 0">
|
||||
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0" size="0.005 0.005 0.005" type="box"></geom>
|
||||
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0.5 0 0 0" size="1 0.005 0.005" type="box"></geom>
|
||||
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0 0.5 0" size="0.005 1 0.001" type="box"></geom>
|
||||
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0.5 0.5 0 0" size="0.005 0.005 1" type="box"></geom>
|
||||
</body>
|
||||
|
||||
<body name="link0" pos="1.09 0.28 0.655">
|
||||
<geom name="bb" type="mesh" mesh="link_base" material="robot0:base_mat" rgba="1 1 1 1"/>
|
||||
<body name="link1" pos="0 0 0.267">
|
||||
<inertial pos="-0.0042142 0.02821 -0.0087788" quat="0.917781 -0.277115 0.0606681 0.277858" mass="0.42603" diaginertia="0.00144551 0.00137757 0.000823511" />
|
||||
<joint name="joint1" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="10" frictionloss="1" />
|
||||
<geom name="j1" type="mesh" mesh="link1" material="robot0:arm_mat" rgba="1 1 1 1"/>
|
||||
<body name="link2" pos="0 0 0" quat="0.707105 -0.707108 0 0">
|
||||
<inertial pos="-3.3178e-05 -0.12849 0.026337" quat="0.447793 0.894132 -0.00224061 0.00218314" mass="0.56095" diaginertia="0.00319151 0.00311598 0.000980804" />
|
||||
<joint name="joint2" pos="0 0 0" axis="0 0 1" limited="true" range="-2.059 2.0944" damping="10" frictionloss="1" />
|
||||
<geom name="j2" type="mesh" mesh="link2" material="robot0:head_mat" rgba="1 1 1 1"/>
|
||||
<body name="link3" pos="0 -0.293 0" quat="0.707105 0.707108 0 0">
|
||||
<inertial pos="0.04223 -0.023258 -0.0096674" quat="0.883205 0.339803 0.323238 0.000542237" mass="0.44463" diaginertia="0.00133227 0.00119126 0.000780475" />
|
||||
<joint name="joint3" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="5" frictionloss="1" />
|
||||
<geom name="j3" type="mesh" mesh="link3" material="robot0:gripper_mat" rgba="1 1 1 1"/>
|
||||
<body name="link4" pos="0.0525 0 0" quat="0.707105 0.707108 0 0">
|
||||
<inertial pos="0.067148 -0.10732 0.024479" quat="0.0654142 0.483317 -0.738663 0.465298" mass="0.52387" diaginertia="0.00288984 0.00282705 0.000894409" />
|
||||
<joint name="joint4" pos="0 0 0" axis="0 0 1" limited="true" range="-0.19198 3.927" damping="5" frictionloss="1" />
|
||||
<geom name="j4" type="mesh" mesh="link4" material="robot0:arm_mat" rgba="1 1 1 1"/>
|
||||
<body name="link5" pos="0.0775 -0.3425 0" quat="0.707105 0.707108 0 0">
|
||||
<inertial pos="-0.00023397 0.036705 -0.080064" quat="0.981064 -0.19003 0.00637998 0.0369004" mass="0.18554" diaginertia="0.00099553 0.000988613 0.000247126" />
|
||||
<joint name="joint5" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="5" frictionloss="1" />
|
||||
<geom name="j5" type="mesh" material="robot0:gripper_mat" rgba="1 1 1 1" mesh="link5" />
|
||||
<body name="link6" pos="0 0 0" quat="0.707105 0.707108 0 0">
|
||||
<inertial pos="0.058911 0.028469 0.0068428" quat="-0.188705 0.793535 0.166088 0.554173" mass="0.31344" diaginertia="0.000827892 0.000768871 0.000386708" />
|
||||
<joint name="joint6" pos="0 0 0" axis="0 0 1" limited="true" range="-1.69297 3.14159" damping="2" frictionloss="1" />
|
||||
<geom name="j6" type="mesh" material="robot0:gripper_mat" rgba="1 1 1 1" mesh="link6" />
|
||||
<body name="link7" pos="0.076 0.097 0" quat="0.707105 -0.707108 0 0">
|
||||
<inertial pos="-0.000420033 -0.00287433 0.0257078" quat="0.999372 -0.0349129 -0.00605634 0.000551744" mass="0.85624" diaginertia="0.00137671 0.00118744 0.000514968" />
|
||||
<joint name="joint7" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="2" frictionloss="1" />
|
||||
<geom name="j8" material="robot0:gripper_mat" type="mesh" rgba="0.753 0.753 0.753 1" mesh="link7" />
|
||||
<geom name="j9" material="robot0:gripper_mat" type="mesh" rgba="1 1 1 1" mesh="base_link" />
|
||||
<site name="grasp" pos="0 0 0.16" rgba="1 0 0 0" type="sphere" size="0.01" group="1"/>
|
||||
<body name="left_outer_knuckle" pos="0 0.035 0.059098">
|
||||
<inertial pos="0 0.021559 0.015181" quat="0.47789 0.87842 0 0" mass="0.033618" diaginertia="1.9111e-05 1.79089e-05 1.90167e-06" />
|
||||
<joint name="drive_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
|
||||
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="left_outer_knuckle" />
|
||||
<body name="left_finger" pos="0 0.035465 0.042039">
|
||||
<inertial pos="0 -0.016413 0.029258" quat="0.697634 0.115353 -0.115353 0.697634" mass="0.048304" diaginertia="1.88037e-05 1.7493e-05 3.56792e-06" />
|
||||
<joint name="left_finger_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
|
||||
<geom name="j10" material="robot0:gripper_finger_mat" type="mesh" rgba="0 0 0 1" conaffinity="3" contype="2" mesh="left_finger" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
|
||||
<body name="right_hand" pos="0 -0.03 0.05" quat="-0.7071 0 0 0.7071">
|
||||
<site name="ee" pos="0 0 0" rgba="0 0 1 0" type="sphere" group="1"/>
|
||||
<site name="ee_x" pos="0 0 0" size="0.005 .1" quat="0.707105 0.707108 0 0 " rgba="1 0 0 0" type="cylinder" group="1"/>
|
||||
<site name="ee_z" pos="0 0 0" size="0.005 .1" quat="0.707105 0 0 0.707108" rgba="0 0 1 0" type="cylinder" group="1"/>
|
||||
<site name="ee_y" pos="0 0 0" size="0.005 .1" quat="0.707105 0 0.707108 0 " rgba="0 1 0 0" type="cylinder" group="1"/>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
<body name="left_inner_knuckle" pos="0 0.02 0.074098">
|
||||
<inertial pos="1.86601e-06 0.0220468 0.0261335" quat="0.664139 -0.242732 0.242713 0.664146" mass="0.0230126" diaginertia="8.34216e-06 6.0949e-06 2.75601e-06" />
|
||||
<joint name="left_inner_knuckle_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
|
||||
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="left_inner_knuckle" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
|
||||
</body>
|
||||
<body name="right_outer_knuckle" pos="0 -0.035 0.059098">
|
||||
<inertial pos="0 -0.021559 0.015181" quat="0.87842 0.47789 0 0" mass="0.033618" diaginertia="1.9111e-05 1.79089e-05 1.90167e-06" />
|
||||
<joint name="right_outer_knuckle_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
|
||||
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="right_outer_knuckle" />
|
||||
<body name="right_finger" pos="0 -0.035465 0.042039">
|
||||
<inertial pos="0 0.016413 0.029258" quat="0.697634 -0.115356 0.115356 0.697634" mass="0.048304" diaginertia="1.88038e-05 1.7493e-05 3.56779e-06" />
|
||||
<joint name="right_finger_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
|
||||
<geom name="j11" material="robot0:gripper_finger_mat" type="mesh" rgba="0 0 0 1" conaffinity="3" contype="2" mesh="right_finger" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
|
||||
<body name="left_hand" pos="0 0.03 0.05" quat="-0.7071 0 0 0.7071">
|
||||
<site name="ee_2" pos="0 0 0" rgba="1 0 0 0" type="sphere" size="0.01" group="1"/>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
<body name="right_inner_knuckle" pos="0 -0.02 0.074098">
|
||||
<inertial pos="1.866e-06 -0.022047 0.026133" quat="0.66415 0.242702 -0.242721 0.664144" mass="0.023013" diaginertia="8.34209e-06 6.0949e-06 2.75601e-06" />
|
||||
<joint name="right_inner_knuckle_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
|
||||
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="right_inner_knuckle" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
</body>
|
||||
</mujoco>
|
||||
|
|
@ -0,0 +1,145 @@
|
|||
import os
|
||||
|
||||
import mujoco
|
||||
import numpy as np
|
||||
from gymnasium_robotics.envs import robot_env
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm.tasks import mocap
|
||||
|
||||
|
||||
class Base(robot_env.MujocoRobotEnv):
|
||||
"""
|
||||
Superclass for all simxarm environments.
|
||||
Args:
|
||||
xml_name (str): name of the xml environment file
|
||||
gripper_rotation (list): initial rotation of the gripper (given as a quaternion)
|
||||
"""
|
||||
|
||||
def __init__(self, xml_name, gripper_rotation=None):
|
||||
if gripper_rotation is None:
|
||||
gripper_rotation = [0, 1, 0, 0]
|
||||
self.gripper_rotation = np.array(gripper_rotation, dtype=np.float32)
|
||||
self.center_of_table = np.array([1.655, 0.3, 0.63625])
|
||||
self.max_z = 1.2
|
||||
self.min_z = 0.2
|
||||
super().__init__(
|
||||
model_path=os.path.join(os.path.dirname(__file__), "assets", xml_name + ".xml"),
|
||||
n_substeps=20,
|
||||
n_actions=4,
|
||||
initial_qpos={},
|
||||
)
|
||||
|
||||
@property
|
||||
def dt(self):
|
||||
return self.n_substeps * self.model.opt.timestep
|
||||
|
||||
@property
|
||||
def eef(self):
|
||||
return self._utils.get_site_xpos(self.model, self.data, "grasp")
|
||||
|
||||
@property
|
||||
def obj(self):
|
||||
return self._utils.get_site_xpos(self.model, self.data, "object_site")
|
||||
|
||||
@property
|
||||
def robot_state(self):
|
||||
gripper_angle = self._utils.get_joint_qpos(self.model, self.data, "right_outer_knuckle_joint")
|
||||
return np.concatenate([self.eef, gripper_angle])
|
||||
|
||||
def is_success(self):
|
||||
return NotImplementedError()
|
||||
|
||||
def get_reward(self):
|
||||
raise NotImplementedError()
|
||||
|
||||
def _sample_goal(self):
|
||||
raise NotImplementedError()
|
||||
|
||||
def get_obs(self):
|
||||
return self._get_obs()
|
||||
|
||||
def _step_callback(self):
|
||||
self._mujoco.mj_forward(self.model, self.data)
|
||||
|
||||
def _limit_gripper(self, gripper_pos, pos_ctrl):
|
||||
if gripper_pos[0] > self.center_of_table[0] - 0.105 + 0.15:
|
||||
pos_ctrl[0] = min(pos_ctrl[0], 0)
|
||||
if gripper_pos[0] < self.center_of_table[0] - 0.105 - 0.3:
|
||||
pos_ctrl[0] = max(pos_ctrl[0], 0)
|
||||
if gripper_pos[1] > self.center_of_table[1] + 0.3:
|
||||
pos_ctrl[1] = min(pos_ctrl[1], 0)
|
||||
if gripper_pos[1] < self.center_of_table[1] - 0.3:
|
||||
pos_ctrl[1] = max(pos_ctrl[1], 0)
|
||||
if gripper_pos[2] > self.max_z:
|
||||
pos_ctrl[2] = min(pos_ctrl[2], 0)
|
||||
if gripper_pos[2] < self.min_z:
|
||||
pos_ctrl[2] = max(pos_ctrl[2], 0)
|
||||
return pos_ctrl
|
||||
|
||||
def _apply_action(self, action):
|
||||
assert action.shape == (4,)
|
||||
action = action.copy()
|
||||
pos_ctrl, gripper_ctrl = action[:3], action[3]
|
||||
pos_ctrl = self._limit_gripper(
|
||||
self._utils.get_site_xpos(self.model, self.data, "grasp"), pos_ctrl
|
||||
) * (1 / self.n_substeps)
|
||||
gripper_ctrl = np.array([gripper_ctrl, gripper_ctrl])
|
||||
mocap.apply_action(
|
||||
self.model,
|
||||
self._model_names,
|
||||
self.data,
|
||||
np.concatenate([pos_ctrl, self.gripper_rotation, gripper_ctrl]),
|
||||
)
|
||||
|
||||
def _render_callback(self):
|
||||
self._mujoco.mj_forward(self.model, self.data)
|
||||
|
||||
def _reset_sim(self):
|
||||
self.data.time = self.initial_time
|
||||
self.data.qpos[:] = np.copy(self.initial_qpos)
|
||||
self.data.qvel[:] = np.copy(self.initial_qvel)
|
||||
self._sample_goal()
|
||||
self._mujoco.mj_step(self.model, self.data, nstep=10)
|
||||
return True
|
||||
|
||||
def _set_gripper(self, gripper_pos, gripper_rotation):
|
||||
self._utils.set_mocap_pos(self.model, self.data, "robot0:mocap", gripper_pos)
|
||||
self._utils.set_mocap_quat(self.model, self.data, "robot0:mocap", gripper_rotation)
|
||||
self._utils.set_joint_qpos(self.model, self.data, "right_outer_knuckle_joint", 0)
|
||||
self.data.qpos[10] = 0.0
|
||||
self.data.qpos[12] = 0.0
|
||||
|
||||
def _env_setup(self, initial_qpos):
|
||||
for name, value in initial_qpos.items():
|
||||
self.data.set_joint_qpos(name, value)
|
||||
mocap.reset(self.model, self.data)
|
||||
mujoco.mj_forward(self.model, self.data)
|
||||
self._sample_goal()
|
||||
mujoco.mj_forward(self.model, self.data)
|
||||
|
||||
def reset(self):
|
||||
self._reset_sim()
|
||||
return self._get_obs()
|
||||
|
||||
def step(self, action):
|
||||
assert action.shape == (4,)
|
||||
assert self.action_space.contains(action), "{!r} ({}) invalid".format(action, type(action))
|
||||
self._apply_action(action)
|
||||
self._mujoco.mj_step(self.model, self.data, nstep=2)
|
||||
self._step_callback()
|
||||
obs = self._get_obs()
|
||||
reward = self.get_reward()
|
||||
done = False
|
||||
info = {"is_success": self.is_success(), "success": self.is_success()}
|
||||
return obs, reward, done, info
|
||||
|
||||
def render(self, mode="rgb_array", width=384, height=384):
|
||||
self._render_callback()
|
||||
# HACK
|
||||
self.model.vis.global_.offwidth = width
|
||||
self.model.vis.global_.offheight = height
|
||||
return self.mujoco_renderer.render(mode)
|
||||
|
||||
def close(self):
|
||||
if self.mujoco_renderer is not None:
|
||||
self.mujoco_renderer.close()
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
import numpy as np
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm import Base
|
||||
|
||||
|
||||
class Lift(Base):
|
||||
def __init__(self):
|
||||
self._z_threshold = 0.15
|
||||
super().__init__("lift")
|
||||
|
||||
@property
|
||||
def z_target(self):
|
||||
return self._init_z + self._z_threshold
|
||||
|
||||
def is_success(self):
|
||||
return self.obj[2] >= self.z_target
|
||||
|
||||
def get_reward(self):
|
||||
reach_dist = np.linalg.norm(self.obj - self.eef)
|
||||
reach_dist_xy = np.linalg.norm(self.obj[:-1] - self.eef[:-1])
|
||||
pick_completed = self.obj[2] >= (self.z_target - 0.01)
|
||||
obj_dropped = (self.obj[2] < (self._init_z + 0.005)) and (reach_dist > 0.02)
|
||||
|
||||
# Reach
|
||||
if reach_dist < 0.05:
|
||||
reach_reward = -reach_dist + max(self._action[-1], 0) / 50
|
||||
elif reach_dist_xy < 0.05:
|
||||
reach_reward = -reach_dist
|
||||
else:
|
||||
z_bonus = np.linalg.norm(np.linalg.norm(self.obj[-1] - self.eef[-1]))
|
||||
reach_reward = -reach_dist - 2 * z_bonus
|
||||
|
||||
# Pick
|
||||
if pick_completed and not obj_dropped:
|
||||
pick_reward = self.z_target
|
||||
elif (reach_dist < 0.1) and (self.obj[2] > (self._init_z + 0.005)):
|
||||
pick_reward = min(self.z_target, self.obj[2])
|
||||
else:
|
||||
pick_reward = 0
|
||||
|
||||
return reach_reward / 100 + pick_reward
|
||||
|
||||
def _get_obs(self):
|
||||
eef_velp = self._utils.get_site_xvelp(self.model, self.data, "grasp") * self.dt
|
||||
gripper_angle = self._utils.get_joint_qpos(self.model, self.data, "right_outer_knuckle_joint")
|
||||
eef = self.eef - self.center_of_table
|
||||
|
||||
obj = self.obj - self.center_of_table
|
||||
obj_rot = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")[-4:]
|
||||
obj_velp = self._utils.get_site_xvelp(self.model, self.data, "object_site") * self.dt
|
||||
obj_velr = self._utils.get_site_xvelr(self.model, self.data, "object_site") * self.dt
|
||||
|
||||
obs = np.concatenate(
|
||||
[
|
||||
eef,
|
||||
eef_velp,
|
||||
obj,
|
||||
obj_rot,
|
||||
obj_velp,
|
||||
obj_velr,
|
||||
eef - obj,
|
||||
np.array(
|
||||
[
|
||||
np.linalg.norm(eef - obj),
|
||||
np.linalg.norm(eef[:-1] - obj[:-1]),
|
||||
self.z_target,
|
||||
self.z_target - obj[-1],
|
||||
self.z_target - eef[-1],
|
||||
]
|
||||
),
|
||||
gripper_angle,
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": eef}
|
||||
|
||||
def _sample_goal(self):
|
||||
# Gripper
|
||||
gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
|
||||
super()._set_gripper(gripper_pos, self.gripper_rotation)
|
||||
|
||||
# Object
|
||||
object_pos = self.center_of_table - np.array([0.15, 0.10, 0.07])
|
||||
object_pos[0] += self.np_random.uniform(-0.05, 0.05, size=1)
|
||||
object_pos[1] += self.np_random.uniform(-0.05, 0.05, size=1)
|
||||
object_qpos = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")
|
||||
object_qpos[:3] = object_pos
|
||||
self._utils.set_joint_qpos(self.model, self.data, "object_joint0", object_qpos)
|
||||
self._init_z = object_pos[2]
|
||||
|
||||
# Goal
|
||||
return object_pos + np.array([0, 0, self._z_threshold])
|
||||
|
||||
def reset(self):
|
||||
self._action = np.zeros(4)
|
||||
return super().reset()
|
||||
|
||||
def step(self, action):
|
||||
self._action = action.copy()
|
||||
return super().step(action)
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
# import mujoco_py
|
||||
import mujoco
|
||||
import numpy as np
|
||||
|
||||
|
||||
def apply_action(model, model_names, data, action):
|
||||
if model.nmocap > 0:
|
||||
pos_action, gripper_action = np.split(action, (model.nmocap * 7,))
|
||||
if data.ctrl is not None:
|
||||
for i in range(gripper_action.shape[0]):
|
||||
data.ctrl[i] = gripper_action[i]
|
||||
pos_action = pos_action.reshape(model.nmocap, 7)
|
||||
pos_delta, quat_delta = pos_action[:, :3], pos_action[:, 3:]
|
||||
reset_mocap2body_xpos(model, model_names, data)
|
||||
data.mocap_pos[:] = data.mocap_pos + pos_delta
|
||||
data.mocap_quat[:] = data.mocap_quat + quat_delta
|
||||
|
||||
|
||||
def reset(model, data):
|
||||
if model.nmocap > 0 and model.eq_data is not None:
|
||||
for i in range(model.eq_data.shape[0]):
|
||||
# if sim.model.eq_type[i] == mujoco_py.const.EQ_WELD:
|
||||
if model.eq_type[i] == mujoco.mjtEq.mjEQ_WELD:
|
||||
# model.eq_data[i, :] = np.array([0., 0., 0., 1., 0., 0., 0.])
|
||||
model.eq_data[i, :] = np.array(
|
||||
[
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
]
|
||||
)
|
||||
# sim.forward()
|
||||
mujoco.mj_forward(model, data)
|
||||
|
||||
|
||||
def reset_mocap2body_xpos(model, model_names, data):
|
||||
if model.eq_type is None or model.eq_obj1id is None or model.eq_obj2id is None:
|
||||
return
|
||||
|
||||
# For all weld constraints
|
||||
for eq_type, obj1_id, obj2_id in zip(model.eq_type, model.eq_obj1id, model.eq_obj2id, strict=False):
|
||||
# if eq_type != mujoco_py.const.EQ_WELD:
|
||||
if eq_type != mujoco.mjtEq.mjEQ_WELD:
|
||||
continue
|
||||
# body2 = model.body_id2name(obj2_id)
|
||||
body2 = model_names.body_id2name[obj2_id]
|
||||
if body2 == "B0" or body2 == "B9" or body2 == "B1":
|
||||
continue
|
||||
mocap_id = model.body_mocapid[obj1_id]
|
||||
if mocap_id != -1:
|
||||
# obj1 is the mocap, obj2 is the welded body
|
||||
body_idx = obj2_id
|
||||
else:
|
||||
# obj2 is the mocap, obj1 is the welded body
|
||||
mocap_id = model.body_mocapid[obj2_id]
|
||||
body_idx = obj1_id
|
||||
assert mocap_id != -1
|
||||
data.mocap_pos[mocap_id][:] = data.xpos[body_idx]
|
||||
data.mocap_quat[mocap_id][:] = data.xquat[body_idx]
|
||||
|
|
@ -0,0 +1,86 @@
|
|||
import numpy as np
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm import Base
|
||||
|
||||
|
||||
class PegInBox(Base):
|
||||
def __init__(self):
|
||||
super().__init__("peg_in_box")
|
||||
|
||||
def _reset_sim(self):
|
||||
self._act_magnitude = 0
|
||||
super()._reset_sim()
|
||||
for _ in range(10):
|
||||
self._apply_action(np.array([0, 0, 0, 1], dtype=np.float32))
|
||||
self.sim.step()
|
||||
|
||||
@property
|
||||
def box(self):
|
||||
return self.sim.data.get_site_xpos("box_site")
|
||||
|
||||
def is_success(self):
|
||||
return np.linalg.norm(self.obj - self.box) <= 0.05
|
||||
|
||||
def get_reward(self):
|
||||
dist_xy = np.linalg.norm(self.obj[:2] - self.box[:2])
|
||||
dist_xyz = np.linalg.norm(self.obj - self.box)
|
||||
return float(dist_xy <= 0.045) * (2 - 6 * dist_xyz) - 0.2 * np.square(self._act_magnitude) - dist_xy
|
||||
|
||||
def _get_obs(self):
|
||||
eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
|
||||
gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
|
||||
eef, box = self.eef - self.center_of_table, self.box - self.center_of_table
|
||||
|
||||
obj = self.obj - self.center_of_table
|
||||
obj_rot = self.sim.data.get_joint_qpos("object_joint0")[-4:]
|
||||
obj_velp = self.sim.data.get_site_xvelp("object_site") * self.dt
|
||||
obj_velr = self.sim.data.get_site_xvelr("object_site") * self.dt
|
||||
|
||||
obs = np.concatenate(
|
||||
[
|
||||
eef,
|
||||
eef_velp,
|
||||
box,
|
||||
obj,
|
||||
obj_rot,
|
||||
obj_velp,
|
||||
obj_velr,
|
||||
eef - box,
|
||||
eef - obj,
|
||||
obj - box,
|
||||
np.array(
|
||||
[
|
||||
np.linalg.norm(eef - box),
|
||||
np.linalg.norm(eef - obj),
|
||||
np.linalg.norm(obj - box),
|
||||
gripper_angle,
|
||||
]
|
||||
),
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": box}
|
||||
|
||||
def _sample_goal(self):
|
||||
# Gripper
|
||||
gripper_pos = np.array([1.280, 0.295, 0.9]) + self.np_random.uniform(-0.05, 0.05, size=3)
|
||||
super()._set_gripper(gripper_pos, self.gripper_rotation)
|
||||
|
||||
# Object
|
||||
object_pos = gripper_pos - np.array([0, 0, 0.06]) + self.np_random.uniform(-0.005, 0.005, size=3)
|
||||
object_qpos = self.sim.data.get_joint_qpos("object_joint0")
|
||||
object_qpos[:3] = object_pos
|
||||
self.sim.data.set_joint_qpos("object_joint0", object_qpos)
|
||||
|
||||
# Box
|
||||
box_pos = np.array([1.61, 0.18, 0.58])
|
||||
box_pos[:2] += self.np_random.uniform(-0.11, 0.11, size=2)
|
||||
box_qpos = self.sim.data.get_joint_qpos("box_joint0")
|
||||
box_qpos[:3] = box_pos
|
||||
self.sim.data.set_joint_qpos("box_joint0", box_qpos)
|
||||
|
||||
return self.box
|
||||
|
||||
def step(self, action):
|
||||
self._act_magnitude = np.linalg.norm(action[:3])
|
||||
return super().step(action)
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
import numpy as np
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm import Base
|
||||
|
||||
|
||||
class Push(Base):
|
||||
def __init__(self):
|
||||
super().__init__("push")
|
||||
|
||||
def _reset_sim(self):
|
||||
self._act_magnitude = 0
|
||||
super()._reset_sim()
|
||||
|
||||
def is_success(self):
|
||||
return np.linalg.norm(self.obj - self.goal) <= 0.05
|
||||
|
||||
def get_reward(self):
|
||||
dist = np.linalg.norm(self.obj - self.goal)
|
||||
penalty = self._act_magnitude**2
|
||||
return -(dist + 0.15 * penalty)
|
||||
|
||||
def _get_obs(self):
|
||||
eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
|
||||
gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
|
||||
eef, goal = self.eef - self.center_of_table, self.goal - self.center_of_table
|
||||
|
||||
obj = self.obj - self.center_of_table
|
||||
obj_rot = self.sim.data.get_joint_qpos("object_joint0")[-4:]
|
||||
obj_velp = self.sim.data.get_site_xvelp("object_site") * self.dt
|
||||
obj_velr = self.sim.data.get_site_xvelr("object_site") * self.dt
|
||||
|
||||
obs = np.concatenate(
|
||||
[
|
||||
eef,
|
||||
eef_velp,
|
||||
goal,
|
||||
obj,
|
||||
obj_rot,
|
||||
obj_velp,
|
||||
obj_velr,
|
||||
eef - goal,
|
||||
eef - obj,
|
||||
obj - goal,
|
||||
np.array(
|
||||
[
|
||||
np.linalg.norm(eef - goal),
|
||||
np.linalg.norm(eef - obj),
|
||||
np.linalg.norm(obj - goal),
|
||||
gripper_angle,
|
||||
]
|
||||
),
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": goal}
|
||||
|
||||
def _sample_goal(self):
|
||||
# Gripper
|
||||
gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
|
||||
super()._set_gripper(gripper_pos, self.gripper_rotation)
|
||||
|
||||
# Object
|
||||
object_pos = self.center_of_table - np.array([0.25, 0, 0.07])
|
||||
object_pos[0] += self.np_random.uniform(-0.08, 0.08, size=1)
|
||||
object_pos[1] += self.np_random.uniform(-0.08, 0.08, size=1)
|
||||
object_qpos = self.sim.data.get_joint_qpos("object_joint0")
|
||||
object_qpos[:3] = object_pos
|
||||
self.sim.data.set_joint_qpos("object_joint0", object_qpos)
|
||||
|
||||
# Goal
|
||||
self.goal = np.array([1.600, 0.200, 0.545])
|
||||
self.goal[:2] += self.np_random.uniform(-0.1, 0.1, size=2)
|
||||
self.sim.model.site_pos[self.sim.model.site_name2id("target0")] = self.goal
|
||||
return self.goal
|
||||
|
||||
def step(self, action):
|
||||
self._act_magnitude = np.linalg.norm(action[:3])
|
||||
return super().step(action)
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
import numpy as np
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm import Base
|
||||
|
||||
|
||||
class Reach(Base):
|
||||
def __init__(self):
|
||||
super().__init__("reach")
|
||||
|
||||
def _reset_sim(self):
|
||||
self._act_magnitude = 0
|
||||
super()._reset_sim()
|
||||
|
||||
def is_success(self):
|
||||
return np.linalg.norm(self.eef - self.goal) <= 0.05
|
||||
|
||||
def get_reward(self):
|
||||
dist = np.linalg.norm(self.eef - self.goal)
|
||||
penalty = self._act_magnitude**2
|
||||
return -(dist + 0.15 * penalty)
|
||||
|
||||
def _get_obs(self):
|
||||
eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
|
||||
gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
|
||||
eef, goal = self.eef - self.center_of_table, self.goal - self.center_of_table
|
||||
obs = np.concatenate(
|
||||
[eef, eef_velp, goal, eef - goal, np.array([np.linalg.norm(eef - goal), gripper_angle])], axis=0
|
||||
)
|
||||
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": goal}
|
||||
|
||||
def _sample_goal(self):
|
||||
# Gripper
|
||||
gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
|
||||
super()._set_gripper(gripper_pos, self.gripper_rotation)
|
||||
|
||||
# Goal
|
||||
self.goal = np.array([1.550, 0.287, 0.580])
|
||||
self.goal[:2] += self.np_random.uniform(-0.125, 0.125, size=2)
|
||||
self.sim.model.site_pos[self.sim.model.site_name2id("target0")] = self.goal
|
||||
return self.goal
|
||||
|
||||
def step(self, action):
|
||||
self._act_magnitude = np.linalg.norm(action[:3])
|
||||
return super().step(action)
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
"""
|
||||
Various positional encodings for the transformer.
|
||||
"""
|
||||
|
||||
import math
|
||||
|
||||
import torch
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ Copy-paste from torch.nn.Transformer with modifications:
|
|||
* extra LN at the end of encoder is removed
|
||||
* decoder returns a stack of activations from all decoding layers
|
||||
"""
|
||||
|
||||
import copy
|
||||
from typing import Optional
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ Misc functions, including distributed helpers.
|
|||
|
||||
Mostly copy-paste from torchvision references.
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import os
|
||||
import pickle
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@
|
|||
A collection of utilities for working with nested tensor structures consisting
|
||||
of numpy arrays and torch tensors.
|
||||
"""
|
||||
|
||||
import collections
|
||||
|
||||
import numpy as np
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ def get_safe_torch_device(cfg_device: str, log: bool = False) -> torch.device:
|
|||
return device
|
||||
|
||||
|
||||
def set_seed(seed):
|
||||
def set_global_seed(seed):
|
||||
"""Set seed for reproducibility."""
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
|
|
|
|||
|
|
@ -27,6 +27,7 @@ fps: ???
|
|||
offline_prioritized_sampler: true
|
||||
|
||||
n_action_steps: ???
|
||||
n_obs_steps: ???
|
||||
env: ???
|
||||
|
||||
policy: ???
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
# @package _global_
|
||||
|
||||
n_action_steps: 1
|
||||
n_obs_steps: 1
|
||||
|
||||
policy:
|
||||
name: tdmpc
|
||||
|
|
|
|||
|
|
@ -50,7 +50,7 @@ from lerobot.common.envs.factory import make_env
|
|||
from lerobot.common.logger import log_output_dir
|
||||
from lerobot.common.policies.abstract import AbstractPolicy
|
||||
from lerobot.common.policies.factory import make_policy
|
||||
from lerobot.common.utils import get_safe_torch_device, init_logging, set_seed
|
||||
from lerobot.common.utils import get_safe_torch_device, init_logging, set_global_seed
|
||||
|
||||
|
||||
def write_video(video_path, stacked_frames, fps):
|
||||
|
|
@ -188,7 +188,7 @@ def eval(cfg: dict, out_dir=None, stats_path=None):
|
|||
|
||||
torch.backends.cudnn.benchmark = True
|
||||
torch.backends.cuda.matmul.allow_tf32 = True
|
||||
set_seed(cfg.seed)
|
||||
set_global_seed(cfg.seed)
|
||||
|
||||
log_output_dir(out_dir)
|
||||
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ from lerobot.common.datasets.factory import make_offline_buffer
|
|||
from lerobot.common.envs.factory import make_env
|
||||
from lerobot.common.logger import Logger, log_output_dir
|
||||
from lerobot.common.policies.factory import make_policy
|
||||
from lerobot.common.utils import format_big_number, get_safe_torch_device, init_logging, set_seed
|
||||
from lerobot.common.utils import format_big_number, get_safe_torch_device, init_logging, set_global_seed
|
||||
from lerobot.scripts.eval import eval_policy
|
||||
|
||||
|
||||
|
|
@ -122,7 +122,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
|
|||
|
||||
torch.backends.cudnn.benchmark = True
|
||||
torch.backends.cuda.matmul.allow_tf32 = True
|
||||
set_seed(cfg.seed)
|
||||
set_global_seed(cfg.seed)
|
||||
|
||||
logging.info("make_offline_buffer")
|
||||
offline_buffer = make_offline_buffer(cfg)
|
||||
|
|
@ -224,7 +224,22 @@ def train(cfg: dict, out_dir=None, job_name=None):
|
|||
policy=td_policy,
|
||||
auto_cast_to_device=True,
|
||||
)
|
||||
assert len(rollout) <= cfg.env.episode_length
|
||||
|
||||
assert (
|
||||
len(rollout.batch_size) == 2
|
||||
), "2 dimensions expected: number of env in parallel x max number of steps during rollout"
|
||||
|
||||
num_parallel_env = rollout.batch_size[0]
|
||||
if num_parallel_env != 1:
|
||||
# TODO(rcadene): when num_parallel_env > 1, rollout["episode"] needs to be properly set and we need to add tests
|
||||
raise NotImplementedError()
|
||||
|
||||
num_max_steps = rollout.batch_size[1]
|
||||
assert num_max_steps <= cfg.env.episode_length
|
||||
|
||||
# reshape to have a list of steps to insert into online_buffer
|
||||
rollout = rollout.reshape(num_parallel_env * num_max_steps)
|
||||
|
||||
# set same episode index for all time steps contained in this rollout
|
||||
rollout["episode"] = torch.tensor([env_step] * len(rollout), dtype=torch.int)
|
||||
online_buffer.extend(rollout)
|
||||
|
|
|
|||
|
|
@ -338,73 +338,6 @@ files = [
|
|||
{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cython"
|
||||
version = "3.0.9"
|
||||
description = "The Cython compiler for writing C extensions in the Python language."
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
|
||||
files = [
|
||||
{file = "Cython-3.0.9-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:296bd30d4445ac61b66c9d766567f6e81a6e262835d261e903c60c891a6729d3"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f496b52845cb45568a69d6359a2c335135233003e708ea02155c10ce3548aa89"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:858c3766b9aa3ab8a413392c72bbab1c144a9766b7c7bfdef64e2e414363fa0c"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c0eb1e6ef036028a52525fd9a012a556f6dd4788a0e8755fe864ba0e70cde2ff"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:c8191941073ea5896321de3c8c958fd66e5f304b0cd1f22c59edd0b86c4dd90d"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:e32b016030bc72a8a22a1f21f470a2f57573761a4f00fbfe8347263f4fbdb9f1"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-win32.whl", hash = "sha256:d6f3ff1cd6123973fe03e0fb8ee936622f976c0c41138969975824d08886572b"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-win_amd64.whl", hash = "sha256:56f3b643dbe14449248bbeb9a63fe3878a24256664bc8c8ef6efd45d102596d8"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:35e6665a20d6b8a152d72b7fd87dbb2af6bb6b18a235b71add68122d594dbd41"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f92f4960c40ad027bd8c364c50db11104eadc59ffeb9e5b7f605ca2f05946e20"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:38df37d0e732fbd9a2fef898788492e82b770c33d1e4ed12444bbc8a3b3f89c0"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ad7fd88ebaeaf2e76fd729a8919fae80dab3d6ac0005e28494261d52ff347a8f"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:1365d5f76bf4d19df3d19ce932584c9bb76e9fb096185168918ef9b36e06bfa4"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:c232e7f279388ac9625c3e5a5a9f0078a9334959c5d6458052c65bbbba895e1e"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-win32.whl", hash = "sha256:357e2fad46a25030b0c0496487e01a9dc0fdd0c09df0897f554d8ba3c1bc4872"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-win_amd64.whl", hash = "sha256:1315aee506506e8d69cf6631d8769e6b10131fdcc0eb66df2698f2a3ddaeeff2"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:157973807c2796addbed5fbc4d9c882ab34bbc60dc297ca729504901479d5df7"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:00b105b5d050645dd59e6767bc0f18b48a4aa11c85f42ec7dd8181606f4059e3"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac5536d09bef240cae0416d5a703d298b74c7bbc397da803ac9d344e732d4369"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:09c44501d476d16aaa4cbc29c87f8c0f54fc20e69b650d59cbfa4863426fc70c"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:cc9c3b9f20d8e298618e5ccd32083ca386e785b08f9893fbec4c50b6b85be772"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:a30d96938c633e3ec37000ac3796525da71254ef109e66bdfd78f29891af6454"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-win32.whl", hash = "sha256:757ca93bdd80702546df4d610d2494ef2e74249cac4d5ba9464589fb464bd8a3"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-win_amd64.whl", hash = "sha256:1dc320a9905ab95414013f6de805efbff9e17bb5fb3b90bbac533f017bec8136"},
|
||||
{file = "Cython-3.0.9-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:4ae349960ebe0da0d33724eaa7f1eb866688fe5434cc67ce4dbc06d6a719fbfc"},
|
||||
{file = "Cython-3.0.9-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:63d2537bf688247f76ded6dee28ebd26274f019309aef1eb4f2f9c5c482fde2d"},
|
||||
{file = "Cython-3.0.9-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:36f5a2dfc724bea1f710b649f02d802d80fc18320c8e6396684ba4a48412445a"},
|
||||
{file = "Cython-3.0.9-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:deaf4197d4b0bcd5714a497158ea96a2bd6d0f9636095437448f7e06453cc83d"},
|
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{file = "Cython-3.0.9.tar.gz", hash = "sha256:a2d354f059d1f055d34cfaa62c5b68bc78ac2ceab6407148d47fb508cf3ba4f3"},
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]
|
||||
|
||||
[[package]]
|
||||
name = "debugpy"
|
||||
version = "1.8.1"
|
||||
|
|
@ -639,6 +572,17 @@ files = [
|
|||
[package.extras]
|
||||
test = ["pytest (>=6)"]
|
||||
|
||||
[[package]]
|
||||
name = "farama-notifications"
|
||||
version = "0.0.4"
|
||||
description = "Notifications for all Farama Foundation maintained libraries."
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
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{file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "fasteners"
|
||||
version = "0.19"
|
||||
|
|
@ -840,43 +784,58 @@ files = [
|
|||
protobuf = ["grpcio-tools (>=1.62.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "gym"
|
||||
version = "0.26.2"
|
||||
description = "Gym: A universal API for reinforcement learning environments"
|
||||
name = "gymnasium"
|
||||
version = "0.29.1"
|
||||
description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "gym-0.26.2.tar.gz", hash = "sha256:e0d882f4b54f0c65f203104c24ab8a38b039f1289986803c7d02cdbe214fbcc4"},
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||||
{file = "gymnasium-0.29.1-py3-none-any.whl", hash = "sha256:61c3384b5575985bb7f85e43213bcb40f36fcdff388cae6bc229304c71f2843e"},
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{file = "gymnasium-0.29.1.tar.gz", hash = "sha256:1a532752efcb7590478b1cc7aa04f608eb7a2fdad5570cd217b66b6a35274bb1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cloudpickle = ">=1.2.0"
|
||||
gym_notices = ">=0.0.4"
|
||||
numpy = ">=1.18.0"
|
||||
farama-notifications = ">=0.0.1"
|
||||
numpy = ">=1.21.0"
|
||||
typing-extensions = ">=4.3.0"
|
||||
|
||||
[package.extras]
|
||||
accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
|
||||
all = ["ale-py (>=0.8.0,<0.9.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
|
||||
atari = ["ale-py (>=0.8.0,<0.9.0)"]
|
||||
box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"]
|
||||
classic-control = ["pygame (==2.1.0)"]
|
||||
mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2)"]
|
||||
mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
|
||||
other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)"]
|
||||
testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
|
||||
toy-text = ["pygame (==2.1.0)"]
|
||||
all = ["box2d-py (==2.3.5)", "cython (<3)", "imageio (>=2.14.1)", "jax (>=0.4.0)", "jaxlib (>=0.4.0)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (>=2.3.3)", "mujoco-py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (>=2.1.3)", "shimmy[atari] (>=0.1.0,<1.0)", "swig (==4.*)", "torch (>=1.0.0)"]
|
||||
atari = ["shimmy[atari] (>=0.1.0,<1.0)"]
|
||||
box2d = ["box2d-py (==2.3.5)", "pygame (>=2.1.3)", "swig (==4.*)"]
|
||||
classic-control = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
|
||||
jax = ["jax (>=0.4.0)", "jaxlib (>=0.4.0)"]
|
||||
mujoco = ["imageio (>=2.14.1)", "mujoco (>=2.3.3)"]
|
||||
mujoco-py = ["cython (<3)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "mujoco-py (>=2.1,<2.2)"]
|
||||
other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)", "torch (>=1.0.0)"]
|
||||
testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
|
||||
toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "gym-notices"
|
||||
version = "0.0.8"
|
||||
description = "Notices for gym"
|
||||
name = "gymnasium-robotics"
|
||||
version = "1.2.4"
|
||||
description = "Robotics environments for the Gymnasium repo."
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "gym-notices-0.0.8.tar.gz", hash = "sha256:ad25e200487cafa369728625fe064e88ada1346618526102659b4640f2b4b911"},
|
||||
{file = "gym_notices-0.0.8-py3-none-any.whl", hash = "sha256:e5f82e00823a166747b4c2a07de63b6560b1acb880638547e0cabf825a01e463"},
|
||||
{file = "gymnasium-robotics-1.2.4.tar.gz", hash = "sha256:d304192b066f8b800599dfbe3d9d90bba9b761ee884472bdc4d05968a8bc61cb"},
|
||||
{file = "gymnasium_robotics-1.2.4-py3-none-any.whl", hash = "sha256:c2cb23e087ca0280ae6802837eb7b3a6d14e5bd24c00803ab09f015fcff3eef5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
gymnasium = ">=0.26"
|
||||
imageio = "*"
|
||||
Jinja2 = ">=3.0.3"
|
||||
mujoco = ">=2.3.3,<3.0"
|
||||
numpy = ">=1.21.0"
|
||||
PettingZoo = ">=1.23.0"
|
||||
|
||||
[package.extras]
|
||||
mujoco-py = ["cython (<3)", "mujoco-py (>=2.1,<2.2)"]
|
||||
testing = ["Jinja2 (>=3.0.3)", "PettingZoo (>=1.23.0)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "pytest (==7.0.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "h5py"
|
||||
version = "3.10.0"
|
||||
|
|
@ -1506,25 +1465,6 @@ glfw = "*"
|
|||
numpy = "*"
|
||||
pyopengl = "*"
|
||||
|
||||
[[package]]
|
||||
name = "mujoco-py"
|
||||
version = "2.1.2.14"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "mujoco-py-2.1.2.14.tar.gz", hash = "sha256:eb5b14485acf80a3cf8c15f4b080c6a28a9f79e68869aa696d16cbd51ea7706f"},
|
||||
{file = "mujoco_py-2.1.2.14-py3-none-any.whl", hash = "sha256:37c0b41bc0153a8a0eb3663103a67c60f65467753f74e4ff6e68b879f3e3a71f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cffi = ">=1.10"
|
||||
Cython = ">=0.27.2"
|
||||
fasteners = ">=0.15,<1.0"
|
||||
glfw = ">=1.4.0"
|
||||
imageio = ">=2.1.2"
|
||||
numpy = ">=1.11"
|
||||
|
||||
[[package]]
|
||||
name = "networkx"
|
||||
version = "3.2.1"
|
||||
|
|
@ -1940,6 +1880,31 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
|
|||
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
|
||||
xml = ["lxml (>=4.9.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "pettingzoo"
|
||||
version = "1.24.3"
|
||||
description = "Gymnasium for multi-agent reinforcement learning."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pettingzoo-1.24.3-py3-none-any.whl", hash = "sha256:23ed90517d2e8a7098bdaf5e31234b3a7f7b73ca578d70d1ca7b9d0cb0e37982"},
|
||||
{file = "pettingzoo-1.24.3.tar.gz", hash = "sha256:91f9094f18e06fb74b98f4099cd22e8ae4396125e51719d50b30c9f1c7ab07e6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
gymnasium = ">=0.28.0"
|
||||
numpy = ">=1.21.0"
|
||||
|
||||
[package.extras]
|
||||
all = ["box2d-py (==2.3.5)", "chess (==1.9.4)", "multi-agent-ale-py (==0.1.11)", "pillow (>=8.0.1)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "rlcard (==1.0.5)", "scipy (>=1.4.1)", "shimmy[openspiel] (>=1.2.0)"]
|
||||
atari = ["multi-agent-ale-py (==0.1.11)", "pygame (==2.3.0)"]
|
||||
butterfly = ["pygame (==2.3.0)", "pymunk (==6.2.0)"]
|
||||
classic = ["chess (==1.9.4)", "pygame (==2.3.0)", "rlcard (==1.0.5)", "shimmy[openspiel] (>=1.2.0)"]
|
||||
mpe = ["pygame (==2.3.0)"]
|
||||
other = ["pillow (>=8.0.1)"]
|
||||
sisl = ["box2d-py (==2.3.5)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "scipy (>=1.4.1)"]
|
||||
testing = ["AutoROM", "pre-commit", "pynput", "pytest", "pytest-cov", "pytest-markdown-docs", "pytest-xdist"]
|
||||
|
||||
[[package]]
|
||||
name = "pillow"
|
||||
version = "10.2.0"
|
||||
|
|
@ -3510,4 +3475,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "1a45c808e1c48bcbf4319d4cf6876771b7d50f40a5a8968a8b7f3af36192bf34"
|
||||
content-hash = "99addbfc02bcd35a308f4ecc5b4285c9c5054118f4aadea27650d8bf355d9616"
|
||||
|
|
|
|||
|
|
@ -21,7 +21,6 @@ packages = [{include = "lerobot"}]
|
|||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.10"
|
||||
cython = "^3.0.8"
|
||||
termcolor = "^2.4.0"
|
||||
omegaconf = "^2.3.0"
|
||||
dm-env = "^1.6"
|
||||
|
|
@ -42,9 +41,7 @@ mpmath = "^1.3.0"
|
|||
torch = "^2.2.1"
|
||||
tensordict = {git = "https://github.com/pytorch/tensordict"}
|
||||
torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
|
||||
mujoco = "2.3.7"
|
||||
mujoco-py = "^2.1.2.14"
|
||||
gym = "^0.26.2"
|
||||
mujoco = "^2.3.7"
|
||||
opencv-python = "^4.9.0.80"
|
||||
diffusers = "^0.26.3"
|
||||
torchvision = "^0.17.1"
|
||||
|
|
@ -52,6 +49,8 @@ h5py = "^3.10.0"
|
|||
dm-control = "1.0.14"
|
||||
huggingface-hub = {extras = ["hf-transfer"], version = "^0.21.4"}
|
||||
robomimic = "0.2.0"
|
||||
gymnasium-robotics = "^1.2.4"
|
||||
gymnasium = "^0.29.1"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
|
|
@ -90,7 +89,7 @@ exclude = [
|
|||
|
||||
[tool.ruff.lint]
|
||||
select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
|
||||
|
||||
ignore-init-module-imports = true
|
||||
|
||||
[tool.poetry-dynamic-versioning]
|
||||
enable = true
|
||||
|
|
|
|||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:10ec2f944de18f1a2aa3fc2f9a4185c03e3a5afc31148c85c98b58602ac4186e
|
||||
size 800
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1a589cba6bf0dfce138110864b6509508a804d7ea5c519d0a3cd67c4a87fa2d0
|
||||
size 200
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6afe7098f30bdc8564526517c085e62613f6cb67194153840567974cfa6f3815
|
||||
size 400
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"action": {"device": "cpu", "shape": [50, 4], "dtype": "torch.float32"}, "episode": {"device": "cpu", "shape": [50], "dtype": "torch.int32"}, "frame_id": {"device": "cpu", "shape": [50], "dtype": "torch.int64"}, "shape": [50], "device": "cpu", "_type": "<class 'tensordict._td.TensorDict'>"}
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:dab3a9712c413c4bfcd91c645752ab981306b23d25bcd4da4c422911574673f7
|
||||
size 50
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"reward": {"device": "cpu", "shape": [50], "dtype": "torch.float32"}, "done": {"device": "cpu", "shape": [50], "dtype": "torch.bool"}, "shape": [50], "device": "cpu", "_type": "<class 'tensordict._td.TensorDict'>"}
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d6f9d1422ce3764e7253f70ed4da278f0c07fafef0d5386479f09d6b6b9d8259
|
||||
size 1058400
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"image": {"device": "cpu", "shape": [50, 3, 84, 84], "dtype": "torch.uint8"}, "state": {"device": "cpu", "shape": [50, 4], "dtype": "torch.float32"}, "shape": [50], "device": "cpu", "_type": "<class 'tensordict._td.TensorDict'>"}
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:52e7c1a3c4fb2423b195e66ffee2c9e23b3ea0ad7c8bfc4dec30a35c65cadcbb
|
||||
size 800
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c4dbe8ea1966e5cc6da6daf5704805b9b5810f4575de7016b8f6cb1da1d7bb8a
|
||||
size 200
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8fca8ddbda3f7bb2f6e7553297c18f3ab8f8b73d64b5c9f81a3695ad9379d403
|
||||
size 1058400
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"image": {"device": "cpu", "shape": [50, 3, 84, 84], "dtype": "torch.uint8"}, "state": {"device": "cpu", "shape": [50, 4], "dtype": "torch.float32"}, "shape": [50], "device": "cpu", "_type": "<class 'tensordict._td.TensorDict'>"}
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:7b3e3e12896d553c208ee152f6d447c877c435e15d010c4a6171966d5b8a0c0b
|
||||
size 800
|
||||
Binary file not shown.
|
|
@ -9,10 +9,8 @@ from .utils import DEVICE, init_config
|
|||
@pytest.mark.parametrize(
|
||||
"env_name,dataset_id",
|
||||
[
|
||||
# TODO(rcadene): simxarm is depreciated for now
|
||||
# ("simxarm", "lift"),
|
||||
("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"),
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from lerobot.common.datasets.factory import make_offline_buffer
|
|||
|
||||
from lerobot.common.envs.factory import make_env
|
||||
from lerobot.common.envs.pusht.env import PushtEnv
|
||||
from lerobot.common.envs.simxarm import SimxarmEnv
|
||||
from lerobot.common.envs.simxarm.env import SimxarmEnv
|
||||
|
||||
from .utils import DEVICE, init_config
|
||||
|
||||
|
|
@ -39,19 +39,19 @@ def print_spec_rollout(env):
|
|||
print("data from rollout:", simple_rollout(100))
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Simxarm is deprecated")
|
||||
@pytest.mark.parametrize(
|
||||
"task,from_pixels,pixels_only",
|
||||
[
|
||||
("lift", False, False),
|
||||
("lift", True, False),
|
||||
("lift", True, True),
|
||||
("reach", False, False),
|
||||
("reach", True, False),
|
||||
("push", False, False),
|
||||
("push", True, False),
|
||||
("peg_in_box", False, False),
|
||||
("peg_in_box", True, False),
|
||||
# TODO(aliberts): Add simxarm other tasks
|
||||
# ("reach", False, False),
|
||||
# ("reach", True, False),
|
||||
# ("push", False, False),
|
||||
# ("push", True, False),
|
||||
# ("peg_in_box", False, False),
|
||||
# ("peg_in_box", True, False),
|
||||
],
|
||||
)
|
||||
def test_simxarm(task, from_pixels, pixels_only):
|
||||
|
|
@ -84,7 +84,7 @@ def test_pusht(from_pixels, pixels_only):
|
|||
@pytest.mark.parametrize(
|
||||
"env_name",
|
||||
[
|
||||
# "simxarm",
|
||||
"simxarm",
|
||||
"pusht",
|
||||
"aloha",
|
||||
],
|
||||
|
|
|
|||
|
|
@ -19,12 +19,13 @@ from .utils import DEVICE, init_config
|
|||
[
|
||||
("simxarm", "tdmpc", ["policy.mpc=true"]),
|
||||
("pusht", "tdmpc", ["policy.mpc=false"]),
|
||||
("simxarm", "diffusion", []),
|
||||
("pusht", "diffusion", []),
|
||||
("aloha", "act", ["env.task=sim_insertion_scripted"]),
|
||||
("aloha", "act", ["env.task=sim_insertion_human"]),
|
||||
("aloha", "act", ["env.task=sim_transfer_cube_scripted"]),
|
||||
("aloha", "act", ["env.task=sim_transfer_cube_human"]),
|
||||
# TODO(aliberts): simxarm not working with diffusion
|
||||
# ("simxarm", "diffusion", []),
|
||||
],
|
||||
)
|
||||
def test_concrete_policy(env_name, policy_name, extra_overrides):
|
||||
|
|
@ -45,13 +46,6 @@ def test_concrete_policy(env_name, policy_name, extra_overrides):
|
|||
# Check that we can make the policy object.
|
||||
policy = make_policy(cfg)
|
||||
# Check that we run select_actions and get the appropriate output.
|
||||
if env_name == "simxarm":
|
||||
# TODO(rcadene): Not implemented
|
||||
return
|
||||
if policy_name == "tdmpc":
|
||||
# TODO(alexander-soare): TDMPC does not use n_obs_steps but the environment requires this.
|
||||
with open_dict(cfg):
|
||||
cfg["n_obs_steps"] = 1
|
||||
offline_buffer = make_offline_buffer(cfg)
|
||||
env = make_env(cfg, transform=offline_buffer.transform)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue