Planning Robust Strategies for Constructing Multi-object Arrangements

A crucial challenge in robotics is achieving reliable results in spite of sensing and control uncertainty. In this work, we explore the conformant planning approach to robot manipulation. In particular, we tackle the problem of pushing multiple objects simultaneously to achieve a specified arrangement without external sensing. Conformant planning is a belief-state planning problem. A belief state is the set of all possible states of the world, and the goal is to find a sequence of actions that will bring an initial belief state to a goal belief state. To do forward belief-state planning, we created a deterministic belief-state transition model from supervised learning based on off-line physics simulations. We demonstrate the success of this approach in simulations and physical robot experiments.