Force-Sensing Tensegrity for Investigating Physical Human-Robot Interaction in Compliant Robotic Systems

TL;DR

This paper introduces a force-sensing tensegrity robot prototype designed for physical human-robot interaction, demonstrating its ability to detect and classify various physical interactions to facilitate intuitive communication of human intent.

Contribution

The paper presents the design and preliminary testing of a novel force-sensing tensegrity robot, showcasing its potential for reliable interaction classification in collaborative scenarios.

Findings

Force-sensing array effectively detects diverse physical interactions.

Classifiers trained on interaction data can reliably interpret human intent.

Prototype demonstrates feasibility of tensegrity robots in collaborative tasks.

Abstract

Advancements in the domain of physical human-robot interaction (pHRI) have tremendously improved the ability of humans and robots to communicate, collaborate, and coexist. In particular, compliant robotic systems offer many characteristics that can be leveraged towards enabling physical interactions that more efficiently and intuitively communicate intent, making compliant systems potentially useful in more physically demanding subsets of human-robot collaborative scenarios. Tensegrity robots are an example of compliant systems that are well-suited to physical interactions while still retaining useful rigid properties that make them practical for a variety of applications. In this paper, we present the design and preliminary testing of a 6-bar spherical tensegrity with force-sensing capabilities. Using this prototype, we demonstrate the ability of its force-sensor array to detect a variety of physical interaction types that might arise in a human context. We then train and test a series of classifiers using data from unique and representative interactions in order to demonstrate the feasibility of using this physical modality of sensing to reliably communicate goals and intents from a human operator in a human-robot collaborative setting.