A bootstrappable bio-plausible design for visual pose stabilization

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Shuo Han, Andrea Censi, Andrew D Straw, Richard M Murray
International Conference on Robotics and Automation (ICRA), 2010 (Submitted)

We consider the problem of purely visual pose stabilization of a second-order rigid-body system: how to choose forces and torques, based on the visual input alone, such that the view converges to a memorized goal image. Emphasis has been given to the bio-plausibility of the computation, in the sense that the control laws could be in principle implemented on the neural substrate of simple insects. We show that stabilizing laws can be realized by bilinear/quadratic operations on the visual input. Moreover, the control laws can be ``bootstrapped (learned unsupervisedly) from experience, which further substantiate the bio-plausibility of such computation.