剛接触ダイナミクスにおける深層学習の根本的課題
Fundamental Challenges in Deep Learning for Stiff Contact Dynamics
接触の非平滑性が深層学習によるモデル学習の訓練・汎化・データ効率を著しく劣化させることを、接触剛性を変化させたシミュレーションで実証した研究。
著者: Mihir Parmar, Mathew Halm, Michael Posa
分類: cs.RO
原文アブストラクト
Frictional contact has been extensively studied as the core underlying behavior of legged locomotion and manipulation, and its nearly-discontinuous nature makes planning and control difficult even when an accurate model of the robot is available. Here, we present empirical evidence that learning an accurate model in the first place can be confounded by contact, as modern deep learning approaches are not designed to capture this non-smoothness. We isolate the effects of contact's non-smoothness by varying the mechanical stiffness of a compliant contact simulator. Even for a simple system, we find that stiffness alone dramatically degrades training processes, generalization, and data-efficiency. Our results raise serious questions about simulated testing environments which do not accurately reflect the stiffness of rigid robotic hardware. Significant additional investigation will be necessary to fully understand and mitigate these effects, and we suggest several avenues for future study.
