1 citations · 1 across the 7 of their papers we have counts for
7 papers
Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs
Idan Lev-Yehudi, Michael Novitsky, Moran Barenboim +2
Online planning in continuous state, action, and observation spaces remains challenging for autonomous systems. While Monte Carlo Tree Search (MCTS) scales effectively via sampling…
Previous Knowledge Utilization In Online Anytime Belief Space Planning
Michael Novitsky, Moran Barenboim, Vadim Indelman
Online planning under uncertainty remains a critical challenge in robotics and autonomous systems. While tree search techniques are commonly employed to construct partial future tr…
Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice
Idan Lev-Yehudi, Moran Barenboim, Vadim Indelman
Solving partially observable Markov decision processes (POMDPs) with high dimensional and continuous observations, such as camera images, is required for many real life robotics an…
Online POMDP Planning with Anytime Deterministic Optimality Guarantees
Moran Barenboim, Vadim Indelman
Decision-making under uncertainty is a critical aspect of many practical autonomous systems due to incomplete information. Partially Observable Markov Decision Processes (POMDPs) o…
Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees
Moran Barenboim, Idan Lev-Yehudi, Vadim Indelman
Autonomous agents that operate in the real world must often deal with partial observability, which is commonly modeled as partially observable Markov decision processes (POMDPs). H…
Monte Carlo Planning in Hybrid Belief POMDPs
Moran Barenboim, Moshe Shienman, Vadim Indelman
Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the contex…
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,…