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Paper WeCaT2.3

Kuehn, Johannes (Leibniz University Hanover), Haddadin, Sami (Leibniz University Hanover)

An Artificial Robot Nervous System To Teach Robots How To Feel Pain And Reflexively React To Potentially Damaging Contacts

Scheduled for presentation during the Regular Session "Physical Human-Robot Interaction" (WeCaT2), Wednesday, May 18, 2016, 13:16−13:19, Rm. A3

2016 IEEE International Conference on Robotics and Automation, May 16-21, 2016, Stockholm, Sweden

This information is tentative and subject to change. Compiled on February 21, 2020

Keywords Physical Human-Robot Interaction, Compliance and Impedance Control, Biomimetics

Abstract

In this paper, we introduce the concept of an artificial Robot Nervous System (aRNS) as a novel way of unifying multi-modal physical stimuli sensation with robot pain- reflex movements. We focus on the formalization of robot pain, based on insights from human pain research, as an interpre- tation of tactile sensation. Specifically, pain signals are used to adapt the equilibrium position, stiffness, and feedforward torque of a pain-based impedance controller. The schemes are experimentally validated with the KUKA LWR4+ for simulated and real physical collisions using the BioTac sensor.

 

 

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