Anytime incremental planning with E-Graphs

Robots operating in real world environments need to find motion plans quickly. Robot motion should also be efficient and, when operating among people, predictable. Minimizing a cost function, e.g. path length, can produce short, reasonable paths. Anytime planners are ideal for this since they find an initial solution quickly and then improve solution quality as time permits. In previous work, we introduced the concept of Experience Graphs, which allow search-based planners to find paths with bounded sub-optimality quickly by reusing parts of previous paths where relevant. Here we extend planning with Experience Graphs to work in an anytime fashion so a first solution is found quickly using prior experience. As time allows, the dependence on this experience is reduced in order to produce closer to optimal solutions. We also demonstrate how Experience Graphs provide a new way of approaching incremental planning as they naturally reuse information when the environment, the starting configuration of the robot or the goal configuration change. Experimentally, we demonstrate the anytime and incremental properties of our algorithm on mobile manipulation tasks in both simulation and on a real PR2 robot.