Learning State-Variable Relationships in POMCP: A Framework for Mobile Robots

Abstract

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We address the problem of learning relationships on state variables in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we focus on Partially Observable Monte Carlo Planning (POMCP) and represent the acquired knowledge with a Markov Random Field (MRF). We propose, in particular, a method for learning these relationships on a robot as POMCP is used to plan future actions. Then, we present an algorithm that deals with cases in which the MRF is used on episodes having unlikely states with respect to the equality relationships represented by the MRF. Our approach acquires information from the agent’s action outcomes to adapt online the MRF if a mismatch is detected between the MRF and the true state. We test this technique on two domains, rocksample, a standard rover exploration task, and a problem of velocity regulation in industrial mobile robotic platforms, showing that the MRF adaptation algorithm improves the planning performance with respect to the standard approach, which does not adapt the MRF online. Finally, a ROS-based architecture is proposed, which allows running the MRF learning, the MRF adaptation, and MRF usage in POMCP on real robotic platforms. In this case, we successfully tested the architecture on a Gazebo simulator of rocksample. A video of the experiments is available in the Supplementary Material, and the code of the ROS-based architecture is available online.

Keywords

• planning under uncertainty
• POMCP
• POMDP
• prior knowledge
• Markov Random Fields
• learning