ロボット卓球のための多様なラバーにわたるラケット-ボールバウンス力学の学習
Learning Racket-Ball Bounce Dynamics Across Diverse Rubbers for Robotic Table Tennis
異なるラバー種別（表ソフト、裏ソフト、粒高など）を含む10種類のラケット構成で、ラケットとボールの衝突モデルをガウス過程を用いて学習し、反発係数や接線方向インパルスなどの物理パラメータを推定する手法を提案した。
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著者: Thomas Gossard
分類: cs.RO
原文アブストラクト
Accurate dynamic models for racket-ball bounces are essential for reliable control in robotic table tennis. Existing models typically assume simple linear models and are restricted to inverted rubbers, limiting their ability to generalize across the wide variety of rackets encountered in practice. In this work, we present a unified framework for modeling ball-racket interactions across 10 racket configurations featuring different rubber types, including inverted, anti-spin, and pimpled surfaces. Using a high-speed multi-camera setup with spin estimation, we collect a dataset of racket-ball bounces spanning a broad range of incident velocities and spins. We show that key physical parameters governing rebound, such as the Coefficient of Restitution and tangential impulse response, vary systematically with the impact state and differ significantly across rubbers. To capture these effects while preserving physical interpretability, we estimate the parameters of an impulse-based contact model using Gaussian Processes conditioned on the ball's incoming velocity and spin. The resulting model provides both accurate predictions and uncertainty estimations. Compared to the constant parameter baselines, our approach reduces post-impact velocity and spin prediction errors across all racket types, with the largest improvements observed for nonstandard rubbers. Furthermore, the GP-based model enables online identification of racket dynamics with few observations during gameplay.
