ArticleFrontiers in neurorobotics2024
Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads.
Article in Frontiers in neurorobotics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Understanding human co-manipulation via motion and haptic information to enable future physical human-robotic collaborations.Frontiers in neurorobotics · 2025Article
- Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads.Frontiers in neurorobotics · 2024Article
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Authors and funding
5 authors.
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Abstract
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.
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