Reinforcement Learning for Human-Robot Handovers

Ben Gurion University of the Negev, Israel and Cornell University, USA
October 2019 - Ongoing

Collaborators: Alap Kshirsagar (Cornell), Guy Hoffman (Cornell), Tair Faibish (BGU), Armin Biess (BGU)

This project studies model-based reinforcement learning (RL) to train a robot controller for human-robot object handovers. RL is a promising approach to develop handover policies, but existing methods did not consider important aspects of human-robot handovers, namely large spatial variations in reach locations, moving targets, and generalizing over mass changes induced by the object being handed over. We report on promising benefits, but also limitations of existing RL methods for this HRI task.

Publication: A. Kshirsagar, G. Hoffman and A. Biess "Evaluating Guided Policy Search for Human-Robot Handovers", IEEE Robotics and Automation Letters 6(2):3933-3940, 2021 Link





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