Least Commitment Planning for the Object Scouting Problem

State uncertainty is a primary obstacle to effective long-horizon robot task planning. State uncertainty can be decomposed into spatial uncertainty—resolved using SLAM—and uncertainty about the objects in the environment, formalized as the object scouting problem and modeled using the Locally Observable Markov Decision Process (LOMDP). We introduce a new planning framework specifically designed for object scouting with LOMDPs called the Scouting Partial-Order Planner (SPOP), which exploits the characteristics of partial order and regression planning to plan around knowledge gaps the robot may have about the existence, location, and state of relevant objects in its environment. Our results highlight the benefits of partial-order planning, demonstrating its suitability for object scouting due to its ability to identify absent but task-relevant objects, and show that it outperforms comparable planners in plan length, computation time, and execution time.

Authors

• Max Merlin
• Ziyi Yang
• George Konidaris 0001
• David Paulius

Keywords

• Uncertainty
• Markov decision processes
• Planning
• Delays
• Object recognition
• Intelligent robots
• Object Location
• Markov Decision Process
• Local Observations
• Relevant Objects
• Simultaneous Localization And Mapping
• State Of Uncertainty
• Execution Plan
• Line-of-sight
• Object Classification
• Number Of Objects
• Range Of Sensors
• Starting State
• Simulation Domain
• Robot Navigation
• Peanut Butter
• Average Execution Time
• Constraints In Order
• Scouting
• Mobile Manipulator