On-line Real-time Physics-based Predictive Motion Control with Balance Recovery

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dc.contributor.authorHan, Daseongen_US
dc.contributor.authorNoh, Junyongen_US
dc.contributor.authorJin, Xiaogangen_US
dc.contributor.authorShin), Joseph S. Shin (formerly Sung Y.en_US
dc.contributor.editorB. Levy and J. Kautzen_US
dc.date.accessioned2015-03-03T12:27:49Z
dc.date.available2015-03-03T12:27:49Z
dc.date.issued2014en_US
dc.description.abstractIn this paper, we present an on-line real-time physics-based approach to motion control with contact repositioning based on a low-dimensional dynamics model using example motion data. Our approach first generates a reference motion in run time according to an on-line user request by transforming an example motion extracted from a motion library. Guided by the reference motion, it repeatedly generates an optimal control policy for a small time window one at a time for a sequence of partially overlapping windows, each covering a couple of footsteps of the reference motion, which supports an on-line performance. On top of this, our system dynamics and problem formulation allow to derive closed-form derivative functions by exploiting the low-dimensional dynamics model together with example motion data. These derivative functions and their sparse structures facilitate a real-time performance. Our approach also allows contact foot repositioning so as to robustly respond to an external perturbation or an environmental change as well as to perform locomotion tasks such as stepping on stones effectively.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12323en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleOn-line Real-time Physics-based Predictive Motion Control with Balance Recoveryen_US
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