24 citations · 27 across the 8 of their papers we have counts for
5 papers · 1 filter
Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency
Ori Sztyglic, Andrey Zhitnikov, Vadim Indelman
Partially Observable Markov Decision Processes (POMDPs) are notoriously hard to solve. Most advanced state-of-the-art online solvers leverage ideas of Monte Carlo Tree Search (MCTS…
Epistemic Uncertainty Aware Semantic Localization and Mapping for Inference and Belief Space Planning
Vladimir Tchuiev, Vadim Indelman
We investigate the problem of autonomous object classification and semantic SLAM, which in general exhibits a tight coupling between classification, metric SLAM and planning under…
Probabilistic Loss and its Online Characterization for Simplified Decision Making Under Uncertainty
Andrey Zhitnikov, Vadim Indelman
It is a long-standing objective to ease the computation burden incurred by the decision making process. Identification of this mechanism's sensitivity to simplification has tremend…
Online POMDP Planning via Simplification
Ori Sztyglic, Vadim Indelman
In this paper, we consider online planning in partially observable domains. Solving the corresponding POMDP problem is a very challenging task, particularly in an online setting. O…
iX-BSP: Incremental Belief Space Planning
Elad I. Farhi, Vadim Indelman
Deciding what's next? is a fundamental problem in robotics and Artificial Intelligence. Under belief space planning (BSP), in a partially observable setting, it involves calculatin…