Toward Learning Context-Dependent Tasks from Demonstration for Tendon-Driven Surgical Robots
| Autor: | Huang, Yixuan, Bentley, Michael, Hermans, Tucker, Kuntz, Alan |
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| Rok vydání: | 2021 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Tendon-driven robots, a type of continuum robot, have the potential to reduce the invasiveness of surgery by enabling access to difficult-to-reach anatomical targets. In the future, the automation of surgical tasks for these robots may help reduce surgeon strain in the face of a rapidly growing population. However, directly encoding surgical tasks and their associated context for these robots is infeasible. In this work we take steps toward a system that is able to learn to successfully perform context-dependent surgical tasks by learning directly from a set of expert demonstrations. We present three models trained on the demonstrations conditioned on a vector encoding the context of the demonstration. We then use these models to plan and execute motions for the tendon-driven robot similar to the demonstrations for novel context not seen in the training set. We demonstrate the efficacy of our method on three surgery-inspired tasks. Comment: 7 pages, 6 figures, to be published in the proceedings of the 2021 International Symposium on Medical Robotics (ISMR) |
| Databáze: | arXiv |
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