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Paper WeP111.1

Silvera Tawil, David (The University of Sydney), Rye, David (The University of Sydney), Velonaki, Mari (The University of Sydney)

Touch Modality Interpretation for an EIT-Based Sensitive Skin

Scheduled for presentation during the Regular Sessions "Physical Human-Robot Interaction I" (WeP111), Wednesday, May 11, 2011, 13:40−13:55, Room 5F

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 10, 2019

Keywords Robot Companions and Social Human-Robot Interaction, Physical Human-Robot Interaction, Haptics and Haptic Interfaces

Abstract

During social interaction, humans extract important information from tactile stimuli that improves their understanding of the interaction. The development of a similar capacity in a robot will contribute to the future success of intuitive human-robot interaction. This paper presents a method of touch sensing based on the principle of electrical impedance tomography (EIT) that can be used to implement a large, flexible and stretchable artificial sensitive skin for robots. A classifier based on the “LogitBoost” algorithm is used to classify the modality of six different types of touch on an experimental EIT-based skin. Experiments showed that the modality of touch was correctly classified in approximately 80% of the trials. This is comparable with the experimental accuracy of a human touch recipient. The classification accuracies show significant improvements from previous classification algorithms applied to different artificial sensitive skins.

 

 

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