AI RESEARCH
Vectorized Online POMDP Planning
arXiv CS.AI
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ArXi:2510.27191v3 Announce Type: replace-cross Planning under partial observability is an essential capability of autonomous robots. The Partially Observable Marko Decision Process (POMDP) provides a powerful framework for planning under partial observability problems, capturing the stochastic effects of actions and the limited information available through noisy observations. POMDP solving could benefit tremendously from massive parallelization on today's hardware, but parallelizing POMDP solvers has been challenging.