Least Commitment Planning for the Object Scouting Problem - Preliminary Results

Published: 24 Oct 2024, Last Modified: 06 Nov 2024LEAP 2024 PosterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Partial Order Planning, Object Scouting, Locally Observable MDP
Abstract: State uncertainty is one of the primary obstacles to effective long-horizon task planning in robotics. We tackle state uncertainty by decomposing it into spatial uncertainty—resolved by SLAM—and uncertainty about the objects in the environment, formalized as the object scouting problem and modelled using the Locally Observable Markov Decision Process (LOMDP). We introduce a new planning framework called Scouting Partial Order Planner (SPOP), specifically designed for object scouting with LOMDPs, which exploits the characteristics of partial order and regression planning to plan around gaps in knowledge the robot may have about the existence, location, and state of relevant objects in its environment. Our preliminary results demonstrate how the aspects of partial order planning make it uniquely suited to solving tasks in the object scouting context: SPOP significantly outperforms alternative planners in this setting.
Submission Number: 48
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