SOURCE-LINKED INTELLIGENCE
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.
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T14:57:10.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.