主催: 一般社団法人 日本機械学会
会議名: ロボティクス・メカトロニクス 講演会2020
開催日: 2020/05/27 - 2020/05/30
Previous research on reinforcement learning for continuum robot arms have been dealt with a relatively small number of active degrees of freedom and made experiments of simple tasks such as reaching. We aimed to learn to throw a ball by reinforcement learning in a pneumatically-controlled continuum robot arm that has nine actuators. We adopt Cost-regularized Kernel Regression (CrKR) which uses dynamic movement primitives (DMPs) which is one of the movement primitives. In the simulation, the continuum arm was able to learn how to throw a ball forward. We made the same experiment for our real continuum robot arm and found that the learning progressed in the experiment.