Probabilistic analysis of manipulation tasks: a research agenda

The problem of manipulation planning in the presence of uncertainty is addressed. The worst-case planning techniques introduced in Lozano-Perez, Mason, and Taylor (1984) are reviewed. It is shown that these methods are limited by an information gap inherent to worst-case analysis techniques. As the task uncertainty increases, these methods fail to produce useful information even though a high-quality plan may exist. To fill this gap, the probabilistic backprojection, which describes the likelihood that a given action will achieve the task goal from a given initial state is presented. A constructive definition of the probabilistic backprojection and related probabilistic models of manipulation task mechanics is provided. It is shown how these models unify several past results in manipulation planning. These models capture the fundamental nature of the task behavior, but appear to be very complex. Methods for computing these models are sketched. Efficient computational methods remain unknown.<<ETX>>

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