Hearing the Slide: Acoustic-Guided Constraint Learning for Fast Non-Prehensile Transport

TL;DR

This paper introduces an acoustic-guided learning approach for dynamic friction modeling in robotic object transport, significantly improving accuracy over traditional Coulomb models during fast motions.

Contribution

It presents a novel method to learn a real-time friction model from acoustic signals, enhancing motion planning for non-prehensile object transport.

Findings

Learned friction model reduces object displacement by up to 86%.

Acoustic sensing effectively captures real-world friction variations.

Improved transport accuracy over traditional Coulomb-based methods.

Abstract

Object transport tasks are fundamental in robotic automation, emphasizing the importance of efficient and secure methods for moving objects. Non-prehensile transport can significantly improve transport efficiency, as it enables handling multiple objects simultaneously and accommodating objects unsuitable for parallel-jaw or suction grasps. Existing approaches incorporate constraints based on the Coulomb friction model, which is imprecise during fast motions where inherent mechanical vibrations occur. Imprecise constraints can cause transported objects to slide or even fall off the tray. To address this limitation, we propose a novel method to learn a friction model using acoustic sensing that maps a tray's motion profile to a dynamically conditioned friction coefficient. This learned model enables an optimization-based motion planner to adjust the friction constraint at each control step according to the planned motion at that step. In experiments, we generate time-optimized trajectories for a UR5e robot to transport various objects with constraints using both the standard Coulomb friction model and the learned friction model. Results suggest that the learned friction model reduces object displacement by up to 86.0% compared to the baseline, highlighting the effectiveness of acoustic sensing in learning real-world friction constraints.