Handovers frequently occur in our social environments, making it imperative for a collaborative robotic system to master the skill of handover. In this work, we aim to investigate the relationship between the grip force variation for a human giver and the sensed interaction force-torque in human-human handovers, utilizing a data-driven approach. A Long-Short Term Memory (LSTM) network was trained to use the interaction force-torque in a handover to predict the human grip force variation in advance. Further, we propose to utilize the trained network to cause human-like grip force variation for a robotic giver.
@article{arxiv.2303.16009,
title = {Data-driven Grip Force Variation in Robot-Human Handovers},
author = {Parag Khanna and Mårten Björkman and Christian Smith},
journal= {arXiv preprint arXiv:2303.16009},
year = {2023}
}
Comments
Contributed to "Advances in Close Proximity Human-Robot Collaboration" Workshop in 2022 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2022)