Date of Original Version
Abstract or Description
This paper explores the planning and control of a manipulation task accomplished in conditions of high uncertainty. Statistical techniques, like particle filters, provide a framework for expressing the uncertainty and partial observability of the real world and taking actions to reduce them. We explore a classic manipulation problem of planar batting, but with a new twist of shape, pose and impact uncertainty. We demonstrate a technique for characterizing and reducing this uncertainty using a particle filter coupled with a lookahead planner that maximizes information gain. We show that a two-step planner that first acts for information gain and then acts to maximize the expectation of achieving a desired goal is effective at managing shape, pose and impact uncertainty
Proceedings of the IEEE International Conference on Robotics and Automation, Rome, Italy.