  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 .
