Learning Diverse Skills for Local Navigation under Multi-constraint Optimality

Despite many successful applications of data-driven control in robotics, extracting meaningful diverse behaviors remains a challenge. Typically, task performance needs to be compromised in order to achieve diversity. In many scenarios, task requirements are specified as a multitude of reward terms,...

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Bibliographic Details
Published in2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 5083 - 5089
Main Authors Cheng, Jin, Vlastelica, Marin, Kolev, Pavel, Li, Chenhao, Martius, Georg
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.05.2024
Subjects
Force
Navigation
Optimization
Quadrupedal robots
Task analysis
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Summary:Despite many successful applications of data-driven control in robotics, extracting meaningful diverse behaviors remains a challenge. Typically, task performance needs to be compromised in order to achieve diversity. In many scenarios, task requirements are specified as a multitude of reward terms, each requiring a different trade-off. In this work, we take a constrained optimization viewpoint on the quality-diversity trade-off and show that we can obtain diverse policies while imposing constraints on their value functions which are defined through distinct rewards. In line with previous work, further control of the diversity level can be achieved through an attract-repel reward term motivated by the Van der Waals force. We demonstrate the effectiveness of our method on a local navigation task where a quadruped robot needs to reach the target within a finite horizon. Finally, our trained policies transfer well to the real 12-DoF quadruped robot, Solo12, and exhibit diverse agile behaviors with successful obstacle traversal.
DOI:10.1109/ICRA57147.2024.10611629