Research Article
Enriching Process Models with Relevant Process Details for Flexible Human-Robot Teaming
@INPROCEEDINGS{10.1007/978-3-031-54531-3_14, author={Myriel Fichtner and Sascha Sucker and Dominik Riedelbauch and Stefan Jablonski and Dominik Henrich}, title={Enriching Process Models with Relevant Process Details for Flexible Human-Robot Teaming}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part III}, proceedings_a={COLLABORATECOM PART 3}, year={2024}, month={2}, keywords={Process Model Optimization Task Annotation Explanation Models Intelligent Robots Process Variety Product Variety}, doi={10.1007/978-3-031-54531-3_14} }- Myriel Fichtner
Sascha Sucker
Dominik Riedelbauch
Stefan Jablonski
Dominik Henrich
Year: 2024
Enriching Process Models with Relevant Process Details for Flexible Human-Robot Teaming
COLLABORATECOM PART 3
Springer
DOI: 10.1007/978-3-031-54531-3_14
Abstract
Human-robot teaming is crucial for future automation in small and medium enterprises. In that context, domain-specific process models are used as an intuitive description of work to share between two agents. Process designers usually introduce a certain degree of abstraction into the models. This way, models are better to trace for humans, and a single model can moreover enable flexibility by capturing several process variations. However, abstraction can lead to unintentional omission of information (e.g., experience of skilled workers). This may impair the quality of process results. To balance the trade-off between model readability and flexibility, we contribute a novel human-robot teaming approach with incremental learning of relevant process details (RPDs). RPDs are extracted from imagery during process execution and used to enrich an integrated process model which unifies human worker instruction and robot programming. Experiments based on two use cases demonstrate the practical feasibility and scalability of our approach.
