{"ID":3053327,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-06T01:20:22.681628739Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04355","arxiv_id":"2606.04355","title":"Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling","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}.","short_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 onli...","url_abs":"https://arxiv.org/abs/2606.04355","url_pdf":"https://arxiv.org/pdf/2606.04355v1","authors":"[\"Yuanchu Liang\",\"Edward Kim\",\"J. Arden Knoll\",\"Wil Thomason\",\"Zachary Kingston\",\"Lydia E. Kavraki\",\"Hanna Kurniawati\"]","published":"2026-06-03T02:14:48Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false,"code_links":[{"ID":612807,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-04T04:41:36.695875263Z","DeletedAt":null,"paper_id":3053327,"paper_url":"https://arxiv.org/abs/2606.04355","paper_title":"Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling","repo_url":"https://github.com/RDLLab/ROPRAS3","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}