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 learn...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2409.15780 |