Learning Racket-Ball Bounce Dynamics Across Diverse Rubbers for Robotic Table Tennis

TL;DR

This paper develops a unified, physics-informed model for racket-ball bounce dynamics across diverse rubber types in robotic table tennis, improving prediction accuracy and enabling online adaptation.

Contribution

It introduces a Gaussian Process-based impulse model that captures racket-specific effects across multiple rubber types, enhancing generalization and interpretability.

Findings

Model reduces velocity and spin prediction errors across all racket types.

Systematic variation of physical parameters with impact state and rubber type.

Enables online identification of racket dynamics during gameplay.

Abstract

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.