mlsanityData to 14 Oct 2026

DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning

arXiv 2410.17186 ↗First posted 22 Oct 2024cs.RO cs.AIcited by 0

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Abstract (arXiv)

Informative path planning (IPP) is an important planning paradigm for various real-world robotic applications such as environment monitoring. IPP involves planning a path that can learn an accurate belief of the quantity of interest, while adhering to planning constraints. Traditional IPP methods typically require high computation time during execution, giving rise to reinforcement learning (RL) based IPP methods. However, the existing RL-based methods do not consider spatio-temporal environments which involve their own challenges due to variations in environment characteristics. In this paper, we propose DyPNIPP, a robust RL-based IPP framework, designed to operate effectively across spatio-temporal environments with varying dynamics. To achieve this, DyPNIPP incorporates domain randomization to train the agent across diverse environments and introduces a dynamics prediction model to capture and adapt the agent actions to specific environment dynamics. Our extensive experiments in a wildfire environment demonstrate that DyPNIPP outperforms existing RL-based IPP algorithms by significantly improving robustness and performing across diverse environment conditions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 40 references that are in the map. First posted on arXiv 22 Oct 2024.

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27 of 40 references.

References · 27

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15 more
  1. 2018Soft-Robust Actor-Critic Policy-Gradientcited by 37
  2. 2022Robust Imitation Learning against Variations in Environment Dynamicscited by 26
  3. 2018An informative path planning framework for UAV-based terrain monitoringcited by 24
  4. 2023Domain Randomization via Entropy Maximizationcited by 19
  5. 2018Learning Robust Optionscited by 17
  6. 2024Deep Reinforcement Learning-based Large-scale Robot Explorationcited by 16
  7. 2021Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensingcited by 14
  8. 2019Robust Visual Domain Randomization for Reinforcement Learningcited by 13
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  12. 2023Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targetscited by 4
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  14. 2021Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learningcited by 2
  15. 2024OffRIPP: Offline RL-based Informative Path Planningcited by 1

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  3. 2018PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning
  4. 2026Motion Planning in Dynamic Environments: A Survey from Classical to Modern Methods
  5. 2025Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
  6. 2025Attention-based Learning for 3D Informative Path Planning
  7. 2025Accelerating Model-Based Reinforcement Learning with State-Space World Models
  8. 2025Path Planning through Multi-Agent Reinforcement Learning in Dynamic Environments