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

Srujan Deolasee, Siva Kailas, Wenhao Luo, Katia P. Sycara, Woojun Kim
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:196-208, 2026.

Abstract

Informative path planning (IPP) aims to find a path that maximizes information gain while adhering to planning constraints so that robots can learn an accurate belief of the quantity of interest, applicable to various real-world robotic applications such as environment monitoring. Traditional IPP methods typically require high computation time during execution, giving rise to reinforcement learning (RL) based IPP methods. However, existing RL-based approaches largely focus on static spatial environments and do not consider spatio-temporal environments where the underlying dynamics evolve over time. 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-deolasee26a, title = {DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning}, author = {Deolasee, Srujan and Kailas, Siva and Luo, Wenhao and Sycara, Katia P. and Kim, Woojun}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {196--208}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/deolasee26a/deolasee26a.pdf}, url = {https://proceedings.mlr.press/v331/deolasee26a.html}, abstract = {Informative path planning (IPP) aims to find a path that maximizes information gain while adhering to planning constraints so that robots can learn an accurate belief of the quantity of interest, applicable to various real-world robotic applications such as environment monitoring. Traditional IPP methods typically require high computation time during execution, giving rise to reinforcement learning (RL) based IPP methods. However, existing RL-based approaches largely focus on static spatial environments and do not consider spatio-temporal environments where the underlying dynamics evolve over time. 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.} }
Endnote
%0 Conference Paper %T DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning %A Srujan Deolasee %A Siva Kailas %A Wenhao Luo %A Katia P. Sycara %A Woojun Kim %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-deolasee26a %I PMLR %P 196--208 %U https://proceedings.mlr.press/v331/deolasee26a.html %V 331 %X Informative path planning (IPP) aims to find a path that maximizes information gain while adhering to planning constraints so that robots can learn an accurate belief of the quantity of interest, applicable to various real-world robotic applications such as environment monitoring. Traditional IPP methods typically require high computation time during execution, giving rise to reinforcement learning (RL) based IPP methods. However, existing RL-based approaches largely focus on static spatial environments and do not consider spatio-temporal environments where the underlying dynamics evolve over time. 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.
APA
Deolasee, S., Kailas, S., Luo, W., Sycara, K.P. & Kim, W.. (2026). DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:196-208 Available from https://proceedings.mlr.press/v331/deolasee26a.html.

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