Lazy validation of Experience Graphs

Many robot applications involve lifelong planning in relatively static environments e.g. assembling objects or sorting mail in an office building. In these types of scenarios, the robot performs many tasks over a long period of time. Thus, the time required for computing a motion plan becomes a significant concern, prompting the need for a fast and efficient motion planner. Since these environments remain similar in between planning requests, planning from scratch is wasteful. Recently, Experience Graphs (E-Graphs) were proposed to accelerate the planning process by reusing parts of previously computed paths to solve new motion planning queries more efficiently. This work describes a method to improve planning times with E-Graphs given changes in the environment by lazily evaluating the validity of past experiences during the planning process. We show the improvements with our method in a single-arm manipulation domain with simulations on the PR2 robot.