強化学習による二足歩行ロボットの入出力線形化制御器の改善
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning
入出力線形化制御器に学習項を加え、モデル不確かさと入力飽和に対処することで二足歩行ロボットの制御性能を向上させた研究。
著者: Fernando Castañeda, Mathias Wulfman, Ayush Agrawal, Tyler Westenbroek, Claire J. Tomlin, S. Shankar Sastry, Koushil Sreenath
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints. Model uncertainty is common in almost every robotic application and input saturation is present in every real world system. In this paper, we address both challenges for the specific case of bipedal robot control by the use of reinforcement learning techniques. Taking the structure of a standard input-output linearizing controller, we use an additive learned term that compensates for model uncertainty. Moreover, by adding constraints to the learning problem we manage to boost the performance of the final controller when input limits are present. We demonstrate the effectiveness of the designed framework for different levels of uncertainty on the five-link planar walking robot RABBIT.
