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A POMDP Model of Eye-Hand Coordination

Tom Erez, Julian J. Tramper, William D. Smart, and Stan C. A. M. Gielen.
In "Proceedings of the 25th Conference on Artificial Intelligence (AAAI 2011)", pages 952-957, 2011.

This paper present a generative model of eye-hand coordination. We use numerical optimization to solve for the joint behavior of an eye and two hands, deriving the optimal motion pattern from first principles, without imposing hand-crafted heuristics. We model the planar scene as a POMDP with 17 continuous state dimensions. Belief-space optimization is facilitated by using a nominal-belief heuristic, whereby we assume (during planning) that the maximum likelihood observation is always obtained. Since a globally-optimal solution for such a high-dimensional domain is computationally intractable, we employ local optimization in the belief domain by using Differential Dynamic Programming. By solving for a locally-optimal plan through belief space, we generate a motion pattern of mutual coordination between hands and eye: the eye's saccades disambiguate the scene in a task-relevant manner, and the hand' motions anticipate the eye's saccades. Finally, the model is validated through a behavioral experiment, in which human subjects perform the same eye-hand coordination task. We show how simulation is congruent with the experimental results.

Paper: [PDF]

  author = {Erez, Tom and Tramper, Julian J. and Smart, William D. and Gielen, Stan C. A. M.},
  title = {A {POMDP} Model of Eye-Hand Coordination},
  booktitle = {Proceedings of the 25th Conference on Artificial Intelligence ({AAAI} 2011)},
  pages = {952--957},
  year = {2011}