Journal ArticleHuman-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyadsMielke, Erich; Townsend, Eric; Wingate, David; Salmon, John L; Killpack, Marc D<p>Human teams are able to easily perform collaborative manipulation tasks. However, simultaneously manipulating a large extended object for a robot and human is a difficult task due to the inherent ambiguity in the desired motion. Our approach in this paper is to leverage data from human-human dyad experiments to determine motion intent for a physical human-robot co-manipulation task. We do this by showing that the human-human dyad data exhibits distinct torque triggers for a lateral movement. As an alternative intent estimation method, we also develop a deep neural network based on motion data from human-human trials to predict future trajectories based on past object motion. We then show how force and motion data can be used to determine robot control in a human-robot dyad. Finally, we compare human-human dyad performance to the performance of two controllers that we developed for human-robot co-manipulation. We evaluate these controllers in three-degree-of-freedom planar motion where determining if the task involves rotation or translation is ambiguous.</p>Frontiers in Neurorobotics2024-02-1210561970Frontiers in Neurorobotics181662-5218https://doi.org/10.3389/fnbot.2024.12916942024792National Science Foundation