Extracting generalizable skills from a single plan execution using abstraction-critical state detection

Robotic task planning is computationally challenging. To reduce planning cost and support life-long operation, we must leverage prior planning experience. To this end, we address the problem of extracting reusable and generalizable abstract skills from successful plan executions. In previous work, we introduced a supporting framework, allowing us, theoretically, to extract an abstract skill from a single execution and later automatically adapt it and reuse it in new domains. We also proved that, given a library of such skills, we can significantly reduce the planning effort for new problems. Nevertheless, until now, abstract-skill extraction could only be performed manually. In this paper, we finally close the automation loop and explain how abstract skills can be practically and automatically extracted. We start by analyzing the desired qualities of an abstract skill and formulate skill extraction as an optimization problem. We then develop two extraction algorithms, based on the novel concept of abstraction-critical state detection. As we show experimentally, the approach is independent of any planning domain.

Authors

• Khen Elimelech
• Lydia E. Kavraki
• Moshe Y. Vardi

Keywords

• Automation
• Costs
• Libraries
• Planning
• Task analysis
• Optimization
• Robots
• Execution Plan
• Single Execution
• Optimization Problem
• Extraction Algorithm
• Task Planning
• Succession Planning
• Successful Execution
• Planning Domain
• Maximum Score
• Instructive
• State Space
• Path Planning
• Learning Phase
• Road Map
• Planning Phase
• Transferable Skills
• Pair Of States
• Planning Problem
• Impact Of Length
• Transfer Approach
• Segmentation Scores
• Multiple Skills
• Hill-climbing