24 citations · 27 across the 7 of their papers we have counts for
6 papers · 1 filter
involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs
Gilad Rotman, Vadim Indelman
One of the most complex tasks of decision making and planning is to gather information. This task becomes even more complex when the state is high-dimensional and its belief cannot…
Adaptive Information Belief Space Planning
Moran Barenboim, Vadim Indelman
Reasoning about uncertainty is vital in many real-life autonomous systems. However, current state-of-the-art planning algorithms cannot either reason about uncertainty explicitly,…
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…
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…
Bayesian Incremental Inference Update by Re-using Calculations from Belief Space Planning: A New Paradigm
Elad I. Farhi, Vadim Indelman
Inference and decision making under uncertainty are key processes in every autonomous system and numerous robotic problems. In recent years, the similarities between inference and…