実在する人間の関節可動域を物理シミュレーションで再現するデータ駆動手法
Data-Driven Approach to Simulating Realistic Human Joint Constraints
実人間のデータから関節可動域の境界をニューラルネットで学習し、物理エンジン上で現実的な関節制約を効率的に再現する手法を提案した。
著者: Yifeng Jiang, C. Karen Liu
分類: cs.RO
原文アブストラクト
Modeling realistic human joint limits is important for applications involving physical human-robot interaction. However, setting appropriate human joint limits is challenging because it is pose-dependent: the range of joint motion varies depending on the positions of other bones. The paper introduces a new technique to accurately simulate human joint limits in physics simulation. We propose to learn an implicit equation to represent the boundary of valid human joint configurations from real human data. The function in the implicit equation is represented by a fully connected neural network whose gradients can be efficiently computed via back-propagation. Using gradients, we can efficiently enforce realistic human joint limits through constraint forces in a physics engine or as constraints in an optimization problem.
