Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling
2026-06-03 • Robotics
Robotics
AI summaryⓘ
The authors developed a new method, ROP-RAS3, to help robots make good decisions when they can't fully see or predict their environment. Instead of checking every possible action, their method quickly samples important actions to plan better and faster over long periods. They tested it on complex problems and found it often works better than other top methods. They also showed it working on a real robot. Their approach focuses on efficiency by sampling states and actions wisely, improving planning under uncertainty.
POMDPmotion planningonline planningsamplingbelief spacemacro actionslong-horizon planningstate spaceroboticsuncertainty
Authors
Yuanchu Liang, Edward Kim, J. Arden Knoll, Wil Thomason, Zachary Kingston, Lydia E. Kavraki, Hanna Kurniawati
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}.