A Gaussian Process model learns impact-state-dependent bounce parameters for an impulse contact model across 10 diverse racket rubbers, reducing velocity and spin prediction errors versus constant baselines and supporting online adaptation.
Physics-informed neural networks to model and control robots: A theoretical and experimental investiga- tion
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Learning Racket-Ball Bounce Dynamics Across Diverse Rubbers for Robotic Table Tennis
A Gaussian Process model learns impact-state-dependent bounce parameters for an impulse contact model across 10 diverse racket rubbers, reducing velocity and spin prediction errors versus constant baselines and supporting online adaptation.