触覚情報を用いた布地ナビゲーションの学習
Learning to Navigate Cloth using Haptics
触覚センサ付きアームが布地に絡まらずに袖やズボンなどを通り抜ける制御器を強化学習で訓練し、シミュレーションで有効性を示した論文。
著者: Alexander Clegg, Wenhao Yu, Zackory Erickson, Jie Tan, C. Karen Liu, Greg Turk
分類: cs.RO
原文アブストラクト
We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.
