Learning and Reasoning for Robot Sequential Decision Making under Uncertainty

TL;DR

This paper introduces LCORPP, a comprehensive framework for robot sequential decision-making that integrates supervised learning, reasoning with human knowledge, and planning under uncertainty, demonstrated through human intention estimation tasks.

Contribution

The work presents a novel hybrid reasoning framework that combines learning, reasoning, and planning for improved robot decision-making under uncertainty.

Findings

Outperforms no-learning and no-reasoning baselines in accuracy.

Achieves higher efficiency in human intention estimation.

Effective in office environment scenarios.

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.