Learning and Reasoning for Robot Sequential Decision Making under Uncertainty

Saeid Amiri; Mohammad Shokrolah Shirazi; Shiqi Zhang

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arXiv · 1901.05322

Learning and Reasoning for Robot Sequential Decision Making under Uncertainty

Saeid Amiri·Mohammad Shokrolah Shirazi·Shiqi Zhang

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Abstract

Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.

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Saeid Amiri, Mohammad Shokrolah Shirazi, Shiqi Zhang. 2019-01-16. Learning and Reasoning for Robot Sequential Decision Making under Uncertainty. https://arxiv.org/abs/1901.05322

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