Human-Machine Collaborative Systems for Microsurgical Applications
International Journal of Robotics Research
Motion intention recognition in robot assisted applications
Robotics and Autonomous Systems
Design considerations and human-machine performance of moving virtual fixtures
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
HAPTICS'04 Proceedings of the 12th international conference on Haptic interfaces for virtual environment and teleoperator systems
Assistive teleoperation for manipulation tasks
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
A policy-blending formalism for shared control
International Journal of Robotics Research
Multimedia Tools and Applications
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Hidden Markov Models (HMMs) are used for automatic segmentation and recognition of user motions. A new algorithm for real-time HMM recognition was developed. The segmentation results are used to provide appropriate assistance in a combined curve following and object avoidance task. This assistance takes the form of a virtual fixture, whose compliance can be altered online. Recognition and assistance experiments were performed using force and position data recorded from a cooperative manipulation system, where a robot and a human operator hold an instrument simultaneously. Recognition accuracy exceeds 90%, even when the users training the HMMs differ from those executing the task. For a task consisting of both path following and avoidance motions, an HMM-based virtual fixture switches the compliance from low to high when the user is trying to move away from the path. The HMM method improves operator performance in comparison with a constant virtual fixture and no virtual fixture.