Extracting generalizable skills from a single plan execution using abstraction-critical state detection
Khen Elimelech,Lydia E. Kavraki,Moshe Y. Vardi,Khen Elimelech,Lydia E. Kavraki,Moshe Y. Vardi
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...
