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ROP-RAS3 introduces a novel sampling-based approach for solving long-horizon Partially Observable Markov Decision Processes (POMDPs), significantly improving scalability by decoupling convergence speed from the size of the action space. The method achieves superior success rates on complex problems with up to 3000 lookahead steps and 35-dimensional state spaces, including hybrid continuous-discrete environments, without requiring exhaustive action enumeration. This advancement offers a practical solution for real-time motion planning under uncertainty, validated by both extensive simulations and physical robot demonstrations.
Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling
arXiv:2606.04355v1 Announce Type: new Abstract: Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs remain difficult to solve. To alleviate the difficulty, this paper proposes a new approximate online POMDP solver, called Reference-Based Online POMDP Planning via Rapid State Space Sampling (ROP-RAS3). ROP-RAS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online, which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space -- a fundamental constraint for modern online POMDP solvers. ROP-RAS3 converges to a near-optimal reference-based solution at a rate that depends on the number of sampled actions, rather than the size of the action space. ROP-RAS3 is evaluated on various long-horizon POMDPs with up to 3000 lookahead steps and 35-dimensional state spaces, where the state, action and observation spaces can be continuous, discrete, or a hybrid of discrete and continuous. Although the reference-based optimal solution may not be the same as the optimal POMDP solution, empirical results indicate that in all of these problems, in terms of success rate, ROP-RAS3 outperforms other state-of-the-art methods by up to multiple folds. We also demonstrate the capability of our approach on a physical robot demonstration. This work extends the theory and empirical results of our ISRR24 paper. Code can be found at \texttt{https://github.com/RDLLab/ROPRAS3}.
Abstract:Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs remain difficult to solve. To alleviate the difficulty, this paper proposes a new approximate online POMDP solver, called Reference-Based Online POMDP Planning via Rapid State Space Sampling (ROP-RAS3). ROP-RAS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online, which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space -- a fundamental constraint for modern online POMDP solvers. ROP-RAS3 converges to a near-optimal reference-based solution at a rate that depends on the number of sampled actions, rather than the size of the action space. ROP-RAS3 is evaluated on various long-horizon POMDPs with up to 3000 lookahead steps and 35-dimensional state spaces, where the state, action and observation spaces can be continuous, discrete, or a hybrid of discrete and continuous. Although the reference-based optimal solution may not be the same as the optimal POMDP solution, empirical results indicate that in all of these problems, in terms of success rate, ROP-RAS3 outperforms other state-of-the-art methods by up to multiple folds. We also demonstrate the capability of our approach on a physical robot demonstration. This work extends the theory and empirical results of our ISRR24 paper. Code can be found at \texttt{this https URL}.
@inproceedings{Liang2026Thinking, title = {Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling}, author = {Yuanchu Liang and Edward Kim and this http URL Knoll and Wil Thomason and Zachary Kingston and Lydia E. Kavraki and Hanna Kurniawati}, year = 2026, booktitle = {International Journal of Robotics Research (to appear)} }
Robotics (cs.RO)
arXiv:2606.04355 [cs.RO]
(or arXiv:2606.04355v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.04355 arXiv-issued DOI via DataCite (pending registration)
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