K. Elimelech, L. E. Kavraki, and M. Y. Vardi, “Automatic Cross-domain Task Plan Transfer by Caching Abstract Skills,” in Algorithmic Foundations of Robotics XV, vol. 25, S. M. LaValle, J. M. O’Kane, M. Otte, D. Sadigh, and P. Tokekar, Eds. Cham, Switzerland: Springer International Publishing, 2023, pp. 470–487.
Solving realistic robotic task planning problems is computationally demanding. To better exploit the planning effort and reduce the future planning cost, it is important to increase the reusability of successful plans. To this end, we suggest a systematic and automatable approach for plan transfer, by rethinking the plan caching procedure. Specifically, instead of caching successful plans in their original domain, we suggest transferring them upon discovery to a dynamically-defined abstract domain and cache them as “abstract skills” there. This technique allows us to maintain a unified, standardized, and compact skill database, to avoid skill redundancy, and to support lifelong operation. Cached skills can later be reconstructed into new domains on demand, and be applied to new tasks, with no human intervention. This is made possible thanks to the novel concept of “abstraction keys.” An abstraction key, when coupled with a skill, provides all the necessary information to cache it, reconstruct it, and transfer it across all domains in which it is applicable—even domains we have yet to encounter. We practically demonstrate the approach by providing two examples of such keys and explain how they can be used in a manipulation planning domain.
PDF preprint: http://kavrakilab.org/publications/elimelech2022-wafr-skills.pdf