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Article overview
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Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads | Erich Mielke
; Eric Townsend
; David Wingate
; Marc D. Killpack
; | Date: |
3 Jan 2020 | 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. | Source: | arXiv, 2001.0991 | Services: | Forum | Review | PDF | Favorites |
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