Task and Motion Planning for Execution in the Real

Abstract

Task and motion planning represents a powerful set of hybrid planning methods that combine reasoning over discrete task domains and continuous motion generation. Traditional reasoning necessitates task domain models and enough information to ground actions to motion planning queries. Gaps in this knowledge often arise from sources such as occlusion or imprecise modeling. This work generates task and motion plans that include actions cannot be fully grounded at planning time. During execution, such an action is handled by a provided human-designed or learned closed-loop behavior. Execution combines offline planned motions and online behaviors till reaching the task goal. Failures of behaviors are fed back as constraints to find new plans. Forty real-robot trials and motivating demonstrations are performed to evaluate the proposed framework and compare it against state-of-the-art. Results show faster execution time, less number of actions, and more success in problems where diverse gaps arise. The experiment data are shared for researchers to simulate these settings. The work shows promise in expanding the applicable class of realistic partially grounded problems that robots can address.