Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human–robot movement coordination. It uses imitation learning to construct a mixture model of human–robot interaction primitives....

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Bibliographic Details
Published in:Autonomous robots 2017-03, Vol.41 (3), p.593-612
Main Authors: Maeda, Guilherme J., Neumann, Gerhard, Ewerton, Marco, Lioutikov, Rudolf, Kroemer, Oliver, Peters, Jan
Format: Article
Language:English
Subjects:
Artificial Intelligence
Collaboration
Computer Imaging
Control
Coordination
Engineering
Human motion
Industrial plants
Learning
Mechatronics
Moving object recognition
Pattern Recognition and Graphics
Probabilistic models
Prostheses
Robot arms
Robot control
Robotics
Robotics and Automation
Robots
Trajectories
Vision
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Summary:This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human–robot movement coordination. It uses imitation learning to construct a mixture model of human–robot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the human–robot interaction. We evaluated the method experimentally with a lightweight robot arm in a variety of assistive scenarios, including the coordinated handover of a bottle to a human, and the collaborative assembly of a toolbox. Potential applications of the method are personal caregiver robots, control of intelligent prosthetic devices, and robot coworkers in factories.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-016-9556-2