Title:
Anticipating Human Actions for Collaboration in the Presence of Task and Sensor Uncertainty
Anticipating Human Actions for Collaboration in the Presence of Task and Sensor Uncertainty
Author(s)
Hawkins, Kelsey P.
Bansal, Shray
Vo, Nam N.
Bobick, Aaron F.
Bansal, Shray
Vo, Nam N.
Bobick, Aaron F.
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Abstract
A representation for structured activities is developed that allows a robot to probabilistically infer which task actions a human is currently performing and to predict which future actions will be executed and when they will occur. The goal is to enable a robot to anticipate collaborative actions in the presence of uncertain sensing and task ambiguity. The system can represent multi-path tasks where the task variations may contain partially ordered actions or even optional actions
that may be skipped altogether. The task is represented by an
AND-OR tree structure from which a probabilistic graphical model is constructed. Inference methods for that model are derived that support a planning and execution system for the robot which attempts to minimize a cost function based upon expected human idle time. We demonstrate the theory in both simulation and actual human-robot performance of a two-way- branch
assembly task. In particular we show that the inference model can robustly anticipate the actions of the human even
in the presence of unreliable or noisy detections because of its
integration of all its sensing information along with knowledge of task structure.
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Date Issued
2014-06
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