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Koen1995/base-S0101-env
base-S0101-env is a machine learning model from Koen1995. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
<p align="center" <img src="assets/media/output.gif" alt="Basic policy" width="75%"/ </p
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Updated Nov 29, 2025
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From the Hugging Face model README
Implements a so-101 robotic arm simulaltion environment for the EnvHub.
np.ndarray.shape = (640, 480, 3).np.ndarray.shape = 6 where each element represents the joint control.SO101Env(
xml_pth=Path("assets/SO-ARM100/Simulation/SO101/scene_with_cube.xml"),
obs_w=640,
obs_h=480)
env = gym.make(
"base-sO101-env-v0",
)
try:
obs, _ = env.reset()
for i in range(10):
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
break
finally:
env.close()
Things I want to do
All robot files are from SO-ARM100
@software{Knight_Standard_Open_SO-100,
author = {Knight, Rob and Kooijmans, Pepijn and Cadene, Remi and Alibert, Simon and Aractingi, Michel and Aubakirova, Dana and Zouitine, Adil and Martino, Russi and Palma, Steven and Pascal, Caroline and Wolf, Thomas},
title = {{Standard Open SO-100 \& SO-101 Arms}},
url = {https://github.com/TheRobotStudio/SO-ARM100}
}
@inproceedings{todorov2012mujoco,
title={MuJoCo: A physics engine for model-based control},
author={Todorov, Emanuel and Erez, Tom and Tassa, Yuval},
booktitle={2012 IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages={5026--5033},
year={2012},
organization={IEEE},
doi={10.1109/IROS.2012.6386109}
}