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Title: Leveraging Submovements for Prediction and Trajectory Planning for Human-Robot Handover
The effectiveness of human-robot interactions critically depends on the success of computational efforts to emulate human inference of intent, anticipation of action, and coordination of movement. To this end, we developed two models that leverage a well described feature of human movement: Gaussian-shaped submovements in velocity profiles, to act as robotic surrogates for human inference and trajectory planning in a handover task. We evaluated both models based on how early in a handover movement the inference model can obtain accurate estimates of handover location and timing, and how similar model trajectories are to human receiver trajectories. Initial results using one participant dyad demonstrate that our inference model can accurately predict location and handover timing, while the trajectory planner can use these predictions to provide a human-like trajectory plan for the robot. This approach delivers promising performance while remaining grounded in physiologically meaningful Gaussian-shaped velocity profiles of human motion.  more » « less
Award ID(s):
1935337 1804550
NSF-PAR ID:
10357669
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments
Page Range / eLocation ID:
247 to 253
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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