OffRIPP: Offline RL-based Informative Path Planning

Srikar Babu Gadipudi, Srujan Deolasee, Siva Kailas, Wenhao Luo, Katia P. Sycara, Woojun Kim
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:1000-1011, 2026.

Abstract

Informative path planning (IPP) is a crucial task in robotics, where an agent designs paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP; however, it typically requires online interaction with the environment, which is risky and expensive in practice. To address this challenge, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding additional interactions, while achieving superior performance and fast computation during execution. Our framework leverages batch-constrained RL to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through evaluations on diverse offline datasets and real-world experiments. The numerical results show that our framework outperforms baseline methods, demonstrating its effectiveness.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-gadipudi26a, title = {OffRIPP: Offline RL-based Informative Path Planning}, author = {Gadipudi, Srikar Babu and 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 = {1000--1011}, 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/gadipudi26a/gadipudi26a.pdf}, url = {https://proceedings.mlr.press/v331/gadipudi26a.html}, abstract = {Informative path planning (IPP) is a crucial task in robotics, where an agent designs paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP; however, it typically requires online interaction with the environment, which is risky and expensive in practice. To address this challenge, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding additional interactions, while achieving superior performance and fast computation during execution. Our framework leverages batch-constrained RL to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through evaluations on diverse offline datasets and real-world experiments. The numerical results show that our framework outperforms baseline methods, demonstrating its effectiveness.} }
Endnote
%0 Conference Paper %T OffRIPP: Offline RL-based Informative Path Planning %A Srikar Babu Gadipudi %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-gadipudi26a %I PMLR %P 1000--1011 %U https://proceedings.mlr.press/v331/gadipudi26a.html %V 331 %X Informative path planning (IPP) is a crucial task in robotics, where an agent designs paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP; however, it typically requires online interaction with the environment, which is risky and expensive in practice. To address this challenge, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding additional interactions, while achieving superior performance and fast computation during execution. Our framework leverages batch-constrained RL to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through evaluations on diverse offline datasets and real-world experiments. The numerical results show that our framework outperforms baseline methods, demonstrating its effectiveness.
APA
Gadipudi, S.B., Deolasee, S., Kailas, S., Luo, W., Sycara, K.P. & Kim, W.. (2026). OffRIPP: Offline RL-based Informative Path Planning. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:1000-1011 Available from https://proceedings.mlr.press/v331/gadipudi26a.html.

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