表現学習による自然なロボットアーム軌道生成
Naturalistic Robot Arm Trajectory Generation via Representation Learning
人間の腕に装着したIMUセンサの動作データから、自己教師あり模倣学習と時空間グラフニューラルネットワークを用いて、支援ロボットによる自然な飲水動作の軌道を生成する手法を提案した。
著者: Jayjun Lee, Adam J. Spiers
分類: cs.RO, cs.LG
原文アブストラクト
The integration of manipulator robots in household environments suggests a need for more predictable and human-like robot motion. This holds especially true for wheelchair-mounted assistive robots that can support the independence of people with paralysis. One method of generating naturalistic motion trajectories is via the imitation of human demonstrators. This paper explores a self-supervised imitation learning method using an autoregressive spatio-temporal graph neural network for an assistive drinking task. We address learning from diverse human motion trajectory data that were captured via wearable IMU sensors on a human arm as the action-free task demonstrations. Observed arm motion data from several participants is used to generate natural and functional drinking motion trajectories for a UR5e robot arm.
