Scalable Rao-Blackwellized Online Planning for High-Dimensional POMDPs
Online planning under uncertainty remains a fundamental challenge for robotic systems operating in partially observable environments with high-dimensional state spaces. While sampling-based POMDP solvers enable approximate decision-making in large or continuous domains, their performance degrades as belief dimensionality increases due to the high variance inherent in Monte Carlo-based estimation. In this work, we extend the Rao-Blackwellized online POMDP (RB-POMDP) framework to improve its generalizability in high-dimensional settings through hybrid continuous-discrete belief representations. By analytically propagating uncertainty associated with marginalized state components during tree...
| Authors | Jiho Lee, Nisar Ahmed, Kyle Hollins Wray et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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