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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…
Simplified POMDP Planning with an Alternative Observation Space and Formal Performance Guarantees
Da Kong, Vadim Indelman
Online planning under uncertainty in partially observable domains is an essential capability in robotics and AI. The partially observable Markov decision process (POMDP) is a mathe…
Simplification of Risk Averse POMDPs with Performance Guarantees
Yaacov Pariente, Vadim Indelman
Risk averse decision making under uncertainty in partially observable domains is a fundamental problem in AI and essential for reliable autonomous agents. In our case, the problem…