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

Maddalena Zuccotto1, Marco Piccinelli1, Alberto Castellini1

  • 1Department of Computer Science, University of Verona, Verona, Italy.

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Summary
This summary is machine-generated.

This study introduces a novel method for robots to learn relationships between state variables in Partially Observable Markov Decision Processes (POMDPs). The approach enhances planning performance by adapting Markov Random Fields (MRFs) online during Partially Observable Monte Carlo Planning (POMCP).

Keywords:
Markov Random FieldsPOMCPPOMDPlearningmobile robot planningplanning under uncertaintyprior knowledge

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Partially Observable Markov Decision Processes (POMDPs) are crucial for decision-making in uncertain environments.
  • Partially Observable Monte Carlo Planning (POMCP) is a key algorithm for planning in POMDPs.
  • Learning relationships between state variables can significantly improve planning efficiency.

Purpose of the Study:

  • To enhance planning performance in POMDPs by learning state variable relationships.
  • To integrate Markov Random Fields (MRFs) with POMCP for improved robotic planning.
  • To develop an adaptive algorithm for MRFs that handles unlikely states during planning.

Main Methods:

  • Proposed a method for learning state variable relationships using MRFs within POMCP.
  • Developed an algorithm for online adaptation of MRFs based on action outcomes and detected mismatches.
  • Implemented a ROS-based architecture for real-time MRF learning, adaptation, and usage in POMCP on robotic platforms.

Main Results:

  • The MRF adaptation algorithm demonstrated improved planning performance compared to standard POMCP.
  • Tested successfully on rocksample (rover exploration) and industrial mobile robot velocity regulation tasks.
  • Validated the ROS-based architecture in a Gazebo simulator for the rocksample domain.

Conclusions:

  • Online adaptation of MRFs significantly enhances POMCP planning performance in partially observable environments.
  • The proposed ROS-based architecture enables practical deployment of adaptive MRF-enhanced POMCP on real robots.
  • This work contributes to more efficient and robust decision-making for autonomous robotic systems.