Task Generalization for Robust One-Shot Robot Programming using Entity-based Resources

With a robust one-shot programming approach from previous work we are able to transfer the advantages of general purpose robots into more unstructured environments like private households or small and medium sized enterprises. From this we enable non-experts to use the capabilities of a kinesthetically guidable manipulator. The main contribution of this work is to extend our behavior-based approach by two generalizations in the task representation model, which were identified in previous work, but not yet examined. With this we introduce novel extensions of our programming paradigm by integrating task generalization in terms of path adaption and object generalization. The proposed extensions allow to generalize task solutions from a demonstration by monitoring resources (e.g. objects in the workspace) and their changes. Integrating task generalization by adapting a demonstrated task to new paths as well as new objects in similar situations raises challenges for both, demonstration and execution phase: During task demonstration it is necessary to identify which aspects can be generalized and during execution we have to distinguish adaptable from faulty situations.