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cs.RO2026
POMDPs for Autonomous Science Exploration
Daniel Guirguis, Nathan Wallace, Hanna Kurniawati +1
Autonomous exploration missions require decision-making under sensor uncertainty and computational constraints, yet integrating scientific representations into POMDP planning has r…
cs.RO2026
Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling
Yuanchu Liang, Edward Kim, J. Arden Knoll +4
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalabil…
cs.RO2026
POMDP-based Object Search with Growing State Space and Hybrid Action Domain
Yongbo Chen, Hesheng Wang, Shoudong Huang +1
Efficiently locating target objects in complex indoor environments with diverse furniture, such as shelves, tables, and beds, is a significant challenge for mobile robots. This dif…