Learning Human-Robot Handshaking Preferences for Quadruped Robots

TL;DR

This paper presents a method for learning personalized handshaking preferences for quadruped robots, enabling more socially acceptable interactions by adapting handshake parameters to individual users.

Contribution

The study introduces a belief model that learns individual preferences for handshake parameters using binary choices, improving social interaction quality for quadruped robots.

Findings

76% of users preferred the optimized handshake parameters

Significant reduction in amplitude and frequency errors

Improved energy efficiency and synchronization

Abstract

Quadruped robots are showing impressive abilities to navigate the real world. If they are to become more integrated into society, social trust in interactions with humans will become increasingly important. Additionally, robots will need to be adaptable to different humans based on individual preferences. In this work, we study the social interaction task of learning optimal handshakes for quadruped robots based on user preferences. While maintaining balance on three legs, we parameterize handshakes with a Central Pattern Generator consisting of an amplitude, frequency, stiffness, and duration. Through 10 binary choices between handshakes, we learn a belief model to fit individual preferences for 25 different subjects. Our results show that this is an effective strategy, with 76% of users feeling happy with their identified optimal handshake parameters, and 20% feeling neutral. Moreover, compared with random and test handshakes, the optimized handshakes have significantly decreased errors in amplitude and frequency, lower Dynamic Time Warping scores, and improved energy efficiency, all of which indicate robot synchronization to the user's preferences. Video results can be found at https://youtu.be/elvPv8mq1KM .