Stochastic plans for robotic manipulation

Geometric uncertainty is unavoidable when programming robots for physical applications. We propose a stochastic framework for manipulation planning where plans are ranked on the basis of expected cost. That is, we express the desirability of states and actions with a cost function and describe uncertainty with probability distributions. We illustrate the approach with a new design for a programmable parts feeder: a mechanism that orients two-dimensional parts using a sequence of open-loop mechanical motions. We present a planning algorithm that accepts an n-sided polygonal part as input and, in time $O(n\sp2)$, generates a stochastically optimal plan for orienting the part.