Anytime Game-Theoretic Planning with Active Reasoning About Humans' Latent States for Human-Centered Robots
This addresses the problem of improving human-robot interaction safety and efficiency for autonomous systems like self-driving cars, though it appears incremental as it combines existing techniques in a novel way.
The paper tackles the problem of human-centered robots needing to account for human cognitive limitations and irrationality during interactions, proposing an anytime game-theoretic planner that integrates iterative reasoning models, a POMDP, and chance-constrained Monte-Carlo belief tree search. The result is a planner that enables robots to actively reason about human latent states in real-time, validated in autonomous driving simulations and user studies for traffic merge negotiations.
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.