Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads

• URL: http://arxiv.org/abs/2001.00991v1
• Date: Fri, 3 Jan 2020 21:23:12 GMT
• Title: Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads
• Authors: Erich Mielke, Eric Townsend, David Wingate, and Marc D. Killpack
• Abstract summary: We use data from human-human dyad experiments to determine motion intent which we use for a physical human-robot co-manipulation task. We develop a deep neural network based on motion data from human-human trials to predict human intent based on past motion.
• Score: 2.7036498789349244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human teams are able to easily perform collaborative manipulation tasks. However, for a robot and human to simultaneously manipulate an extended object is a difficult task using existing methods from the literature. Our approach in this paper is to use data from human-human dyad experiments to determine motion intent which we use for a physical human-robot co-manipulation task. We first present and analyze data from human-human dyads performing co-manipulation tasks. We show that our human-human dyad data has interesting trends including that interaction forces are non-negligible compared to the force required to accelerate an object and that the beginning of a lateral movement is characterized by distinct torque triggers from the leader of the dyad. We also examine different metrics to quantify performance of different dyads. We also develop a deep neural network based on motion data from human-human trials to predict human intent based on past motion. We then show how force and motion data can be used as a basis for robot control in a human-robot dyad. Finally, we compare the performance of two controllers for human-robot co-manipulation to human-human dyad performance.

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