This repository shows the training code for Berkeley Humanoid with IsaacLab.
If you use this work in an academic context, please consider citing the following publications:
@misc{2407.21781,
Author = {Qiayuan Liao and Bike Zhang and Xuanyu Huang and Xiaoyu Huang and Zhongyu Li and Koushil Sreenath},
Title = {Berkeley Humanoid: A Research Platform for Learning-based Control},
Year = {2024},
Eprint = {arXiv:2407.21781},
}
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Install Isaac Lab, see the installation guide. Please use IsaacLab v1.0.0 with IsaacSim 4.0.0.
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Using a python interpreter that has Isaac sLab installed, install the library
cd exts/berkeley_humanoid
python -m pip install -e .
Training an agent with RSL-RL on Velocity-Rough-Berkeley-Humanoid-v0:
# run script for training
${ISAAC_LAB_PATH}/isaaclab.sh -p scripts/rsl_rl/train.py --task Velocity-Rough-Berkeley-Humanoid-v0
# run script for playing
${ISAAC_LAB_PATH}/isaaclab.sh -p scripts/rsl_rl/play.py --task Velocity-Rough-Berkeley-Humanoid-Play-v0
Q: Why doesn't the maximum torque of each joint match the values in the paper?
A: The maximum torque is limited for safety reasons.
Q: Where is the joint armature from?
A: From CAD system.
Q: Why does the friction of each joint so large?
A: The motor we used has large cogging torque, we include it in the friction of the actuator model.