E-graphs: Bootstrapping planning with experience graphs

Abstract

In this paper, we develop an online motion planning approach which learns from its planning episodes (experiences) a graph, an Experience Graph. On the theoretical side, we show that planning with Experience graphs is complete and provides bounds on suboptimality with respect to the graph that represents the original planning problem. Experimentally, we show in simulations and on a physical robot that our approach is particularly suitable for higher-dimensional motion planning tasks such as planning for two armed mobile manipulation. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.