Task and Motion Planning for Execution in the Real

TL;DR

This paper presents a hybrid task and motion planning framework that handles partially grounded actions during execution, improving success rates and efficiency in real-robot tasks with knowledge gaps.

Contribution

It introduces a method combining offline planning with online behavior adaptation to address ungrounded actions during execution.

Findings

Faster execution times compared to state-of-the-art.

Reduced number of actions needed for task completion.

Higher success rates in scenarios with knowledge gaps.

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 like 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 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 is 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.