Michael Novitsky — accepted AI-conference papers

2026

Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs

Idan Lev-Yehudi, Michael Novitsky, Moran Barenboim, Ron Benchetrit, Vadim Indelman

IJCAI 2026

Online planning in continuous state, action, and observation spaces remains challenging for autonomous systems. While Monte Carlo Tree Search (MCTS) scales effectively via sampling, most continuous (PO)MDP solvers do not exploit gradient-based action optimization. We propose Action-Gradient MCTS (AG

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2026

Previous Knowledge Utilization in Online Anytime Belief Space Planning

Michael Novitsky, Moran Barenboim, Vadim Indelman

ICRA 2026poster

Online planning under uncertainty remains a critical challenge in robotics and autonomous systems. While tree search techniques are commonly employed to construct partial future trajectories within computational constraints, most existing methods discard information from previous planning sessions c…

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