6 papers · 1 filter
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 Risk-Averse Planning in POMDPs Using Iterated CVaR Value Function
Yaacov Pariente, Vadim Indelman
We study risk-sensitive planning under partial observability using the dynamic risk measure Iterated Conditional Value-at-Risk (ICVaR). A policy evaluation algorithm for ICVaR is d…
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…
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…
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…
Anytime Probabilistically Constrained Provably Convergent Online Belief Space Planning
Andrey Zhitnikov, Vadim Indelman
Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon th…