On Transient Release Dynamics in Robot Throwing: A Sliding Pivot Model

Abstract

Humans throw projectiles with high speed and accuracy, yet robots still lag behind despite precise control and low latency. A key obstacle is the lack of high-fidelity, tractable models for transient release dynamics, where momentum is exchanged via friction over ~50 ms. We first show that the conventional model combining rigid body dynamics and patch friction (LS model) suffers from pathological behaviors, resulting in poor prediction accuracy. While this can be mitigated using viscous smoothing and implicit integration (ILS model), it incurs a high computational cost. Motivated by the dominant effect of in-hand pivoting during release, we propose a Sliding Pivot (SP) model that simplifies the dynamics by capturing sticking–pivoting–sliding under vanishing normal force. The SP model offers comparable accuracy (within 10% of ILS) while running over 20× faster. Compared to the conventional LS model, SP reduces horizontal velocity error by 40% and angular velocity error by 63%, achieving 2.4 cm and 15.4 degrees mean absolute error for landing position and orientation. These results provide a robust, physically grounded foundation for scalable throwing robots.