Constrained Skill Discovery: Quadruped Locomotion with Unsupervised Reinforcement Learning
<|reference_start|>Constrained Skill Discovery: Quadruped Locomotion with Unsupervised Reinforcement Learning: Representation learning and unsupervised skill discovery can allow robots to acquire diverse and reusable behaviors without the need for task-specific rewards. In this work, we use unsupervised reinforcement learning to learn a latent representation by maximizing the mutual information between skills and states subject to a distance constraint. Our method improves upon prior constrained skill discovery methods by replacing the latent transition maximization with a norm-matching objective. This not only results in a much a richer state space coverage compared to baseline methods, but allows the robot to learn more stable and easily controllable locomotive behaviors. We successfully deploy the learned policy on a real ANYmal quadruped robot and demonstrate that the robot can accurately reach arbitrary points of the Cartesian state space in a zero-shot manner, using only an intrinsic skill discovery and standard regularization rewards.<|reference_end|>
@article{atanassov2024constrained,
    title={Constrained Skill Discovery: Quadruped Locomotion with Unsupervised
  Reinforcement Learning},
    author={Vassil Atanassov, Wanming Yu, Alexander Luis Mitchell, Mark Nicholas
  Finean, Ioannis Havoutis},
    journal={arXiv preprint arXiv:2410.07877},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07877},
    primaryClass={cs.RO}
}