Abstract
Motions reinforce meanings in human-robot communication, when they are relevant and initiated at the right times. Given a task of using motions for an autonomous humanoid robot to communicate, different sequences of relevant motions are generated from the motion library. Each motion in the motion library is stable, but a sequence may cause the robot to be unstable and fall. We are interested in predicting if a sequence of motions will result in a fall, without executing the sequence on the robot. We contribute a novel algorithm, ProFeaSM, that uses only body angles collected during the execution of single motions and interpolations between pairs of motions, to predict whether a sequence will cause the robot to fall. We demonstrate the efficacy of ProFeaSM on the NAO humanoid robot in a real-time simulator, Webots, and on a real NAO and explore the trade-off between precision and recall.
Junyun Tay is in the Nanyang Technological University-Carnegie Mellon University (NTU-CMU) Dual Degree Ph.D. Programme in Engineering (Robotics).
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Acknowledgments
This work was supported by the Singapore Millennium Foundation Research Grant and by award NSF IIS-1218932 of the National Science Foundation. The NTU-CMU Dual Degree PhD Programme in Engineering (Robotics) is funded by the Economic Development Board of Singapore. The views and conclusions contained herein are those of the authors only.
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Tay, J., Chen, IM., Veloso, M. (2016). Fall Prediction for New Sequences of Motions. In: Hsieh, M., Khatib, O., Kumar, V. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 109. Springer, Cham. https://doi.org/10.1007/978-3-319-23778-7_56
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DOI: https://doi.org/10.1007/978-3-319-23778-7_56
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