Co-manipulation with a library of virtual guiding fixtures. Issue 5 (June 2018) - Raiola, Gennaro; Restrepo, Susana; Chevalier, Pauline; Rodriguez-Ayerbe, Pedro; Lamy, Xavier; Tliba, Sami; Stulp, Freek
Abstract Virtual guiding fixtures constrain the movements of a robot to task-relevant trajectories, and have been successfully applied to, for instance, surgical and manufacturing tasks. Whereas previous work has considered guiding fixtures for single tasks, in this paper we propose a library of guiding fixtures for multiple tasks, and propose methods for (1) creating and adding guides based on machine learning; (2) selecting guides on-line based on probabilistic implementation of guiding fixtures; (3) refining existing guides based on an incremental learning method. We demonstrate in an industrial task that a library of guiding fixtures provides an intuitive haptic interface for joint human–robot completion of tasks, and improves performance in terms of task execution time, mental workload and errors.