1 citations · 1 across the 6 of their papers we have counts for
6 papers
Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
Idan Lev-Yehudi, Vadim Indelman
Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding. Tree-based search methods such as Monte Carlo Tree Search (MCTS…
Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Tamir Shazman, Idan Lev-Yehudi, Ron Benchetit +1
Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for…
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…
Anytime Incremental POMDP Planning in Continuous Spaces
Ron Benchetrit, Idan Lev-Yehudi, Andrey Zhitnikov +1
Partially Observable Markov Decision Processes (POMDPs) provide a robust framework for decision-making under uncertainty in applications such as autonomous driving and robotic expl…
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…
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…