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- W3203889368 abstract "A human-centered robot needs to reason about the cognitive limitation and potential irrationality of its human partner to achieve seamless interactions. This paper proposes an anytime game-theoretic planner that integrates iterative reasoning models, a partially observable Markov decision process, and chance-constrained Monte-Carlo belief tree search for robot behavioral planning. Our planner enables a robot to safely and actively reason about its human partner's latent cognitive states (bounded intelligence and irrationality) in real-time to maximize its utility better. We validate our approach in an autonomous driving domain where our behavioral planner and a low-level motion controller hierarchically control an autonomous car to negotiate traffic merges. Simulations and user studies are conducted to show our planner's effectiveness." @default.
- W3203889368 created "2021-10-11" @default.
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- W3203889368 date "2021-09-26" @default.
- W3203889368 modified "2023-09-27" @default.
- W3203889368 title "Anytime Game-Theoretic Planning with Active Reasoning About Humans' Latent States for Human-Centered Robots" @default.
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- W3203889368 doi "https://doi.org/10.48550/arxiv.2109.12490" @default.
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