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Grasp Stability Prediction with Sim-to-Real Transfer from Tactile Sensing

2022-08-04 20:55:09
Zilin Si, Zirui Zhu, Arpit Agarwal, Stuart Anderson, Wenzhen Yuan

Abstract

Robot simulation has been an essential tool for data-driven manipulation tasks. However, most existing simulation frameworks lack either efficient and accurate models of physical interactions with tactile sensors or realistic tactile simulation. This makes the sim-to-real transfer for tactile-based manipulation tasks still challenging. In this work, we integrate simulation of robot dynamics and vision-based tactile sensors by modeling the physics of contact. This contact model uses simulated contact forces at the robot's end-effector to inform the generation of realistic tactile outputs. To eliminate the sim-to-real transfer gap, we calibrate our physics simulator of robot dynamics, contact model, and tactile optical simulator with real-world data, and then we demonstrate the effectiveness of our system on a zero-shot sim-to-real grasp stability prediction task where we achieve an average accuracy of 90.7% on various objects. Experiments reveal the potential of applying our simulation framework to more complicated manipulation tasks. We open-source our simulation framework at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2208.02885

PDF

https://arxiv.org/pdf/2208.02885.pdf


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