Identifying important sensory feedback for learning locomotion skills

Author:

Yu WanmingORCID,Yang Chuanyu,McGreavy ChristopherORCID,Triantafyllidis EleftheriosORCID,Bellegarda GuillaumeORCID,Shafiee Milad,Ijspeert Auke JanORCID,Li ZhibinORCID

Abstract

AbstractRobot motor skills can be acquired by deep reinforcement learning as neural networks to reflect state–action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neural networks, human insights and engineering efforts are often required to select appropriate states through qualitative approaches, such as ablation studies, without a quantitative analysis of the state importance. Here we present a systematic saliency analysis that quantitatively evaluates the relative importance of different feedback states for motor skills learned through deep reinforcement learning. Our approach provides a guideline to identify the most essential feedback states for robot motor learning. By using only the important states including joint positions, gravity vector and base linear and angular velocities, we demonstrate that a simulated quadruped robot can learn various robust locomotion skills. We find that locomotion skills learned only with important states can achieve task performance comparable to the performance of those with more states. This work provides quantitative insights into the impacts of state observations on specific types of motor skills, enabling the learning of a wide range of motor skills with minimal sensing dependencies.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Viability leads to the emergence of gait transitions in learning agile quadrupedal locomotion on challenging terrains;Nature Communications;2024-04-09

3. The future of the labor force: higher cognition and more skills;Humanities and Social Sciences Communications;2024-04-02

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