Learning and Reasoning with Action-Related Places for Robust Mobile Manipulation
This addresses robustness in mobile manipulation for robots, but it is incremental as it builds on existing planning and representation methods.
The paper tackles robust mobile manipulation under uncertainty by introducing Action-Related Places (ARPlaces), a representation of task-related robot base locations as probabilistic collections, and shows that using ARPlaces with a transformational planner leads to more robust and efficient performance in simulated experiments.
We propose the concept of Action-Related Place (ARPlace) as a powerful and flexible representation of task-related place in the context of mobile manipulation. ARPlace represents robot base locations not as a single position, but rather as a collection of positions, each with an associated probability that the manipulation action will succeed when located there. ARPlaces are generated using a predictive model that is acquired through experience-based learning, and take into account the uncertainty the robot has about its own location and the location of the object to be manipulated. When executing the task, rather than choosing one specific goal position based only on the initial knowledge about the task context, the robot instantiates an ARPlace, and bases its decisions on this ARPlace, which is updated as new information about the task becomes available. To show the advantages of this least-commitment approach, we present a transformational planner that reasons about ARPlaces in order to optimize symbolic plans. Our empirical evaluation demonstrates that using ARPlaces leads to more robust and efficient mobile manipulation in the face of state estimation uncertainty on our simulated robot.