University of Cambridge > > CUED Control Group Seminars > Modeling human sensorimotor control for better control of surgical robots

Modeling human sensorimotor control for better control of surgical robots

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If you have a question about this talk, please contact Thiago Burghi.

During everyday interaction with the external world, for example during surgery, our brain graciously deals with a task that control engineers find very challenging – closed-loop control of movement and contact forces with outdated and noisy information that arrives from multiple sensors. Robot-assisted minimally invasive surgery (RAMIS), where a surgeon manipulates a pair of joysticks that teleoperate instruments inside a patient’s body, requires precise control of movement, object and tissue manipulation, and perception. Despite many advantages for both the patient and the surgeon, the full potential of RAMIS and other teleoperation applications is yet to be realized. Two of the major progress-impeding gaps, the lack of touch feedback and limited knowledge of how to measure skill and optimize training, could be bridged by applying models of human sensorimotor control. We use behavioral studies to investigate the sensorimotor system integrates information across time, space, and modalities, for movement, object manipulation, and perception, and how the motor system changes following adaptation and skill acquisition. I will present our recent results about human-centered stability and transparency in teleoperation, about the integration of tactile and kinesthetic information during interaction with virtual objects, and our attempts to understand the acquisition of RAMIS skill, and will conclude with thoughts about how all these can be integrated with machine learning to achieve ultimate human-robot collaboration in RAMIS .

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This talk is part of the CUED Control Group Seminars series.

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