A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards
<|reference_start|>A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards: This work introduces a model-free reinforcement learning framework that enables various modes of motion (quadruped, tripod, or biped) and diverse tasks for legged robot locomotion. We employ a motion-style reward based on a relaxed logarithmic barrier function as a soft constraint, to bias the learning process toward the desired motion style, such as gait, foot clearance, joint position, or body height. The predefined gait cycle is encoded in a flexible manner, facilitating gait adjustments throughout the learning process. Extensive experiments demonstrate that KAIST HOUND, a 45 kg robotic system, can achieve biped, tripod, and quadruped locomotion using the proposed framework; quadrupedal capabilities include traversing uneven terrain, galloping at 4.67 m/s, and overcoming obstacles up to 58 cm (67 cm for HOUND2); bipedal capabilities include running at 3.6 m/s, carrying a 7.5 kg object, and ascending stairs-all performed without exteroceptive input.<|reference_end|>
@article{kim2024a,
    title={A Learning Framework for Diverse Legged Robot Locomotion Using
  Barrier-Based Style Rewards},
    author={Gijeong Kim, Yong-Hoon Lee, Hae-Won Park},
    journal={arXiv preprint arXiv:2409.15780},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15780},
    primaryClass={cs.RO}
}