Abstract
We present an attention-minimizing LQR-based feedback control law for vision-based ball catching. Taking Brockett’s control attention functional as our performance criterion, and under the simplifying assumption that the optimal control law is the sum of a linear time-varying feedback term and a time-varying feedforward term, we show that our control law is stable, and easily obtained as the solution to a finite-dimensional optimization problem over the set of symmetric positive-definite matrices. We perform numerical experiments for robotic ball-catching and compare our control law with a discretized version obtained as the solution to a mathematical programming problem. Like human ball catching, our results also exhibit the familiar transition from open-loop to closed-loop control during the catching movement, and also show improved robustness to spatiotemporal quantization. Our approach is applicable to more general control settings in which multi-tasking must be performed under limited computation and communication resources.
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Jang, C., Lee, Je., Lee, S., Park, F.C. (2014). A Minimum Attention Control Law for Ball Catching. In: Duff, A., Lepora, N.F., Mura, A., Prescott, T.J., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2014. Lecture Notes in Computer Science(), vol 8608. Springer, Cham. https://doi.org/10.1007/978-3-319-09435-9_14
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DOI: https://doi.org/10.1007/978-3-319-09435-9_14
Publisher Name: Springer, Cham
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