Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
This work addresses the challenge of enabling robots to perform complex multi-action tasks with uncertainty, though it appears incremental as it combines existing capabilities into a hybrid framework.
The paper tackles the problem of robot sequential decision-making under uncertainty by introducing the LCORPP framework, which integrates supervised learning, automated reasoning, and planning, resulting in improved efficiency and accuracy compared to no-learning and no-reasoning baselines in office environment experiments.
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.