====== Toward Learning Context-Dependent Tasks from Demonstration for Tendon-Driven Surgical Robots ======
===== Introduction =====
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.

===== Publication =====
  - {{https://arxiv.org/abs/2110.07789 | "Toward Learning Context-Dependent Tasks from Demonstration for Tendon-Driven Surgical Robots"}}, Yixuan Huang, Michael Bentley, Tucker Hermans, and Alan Kuntz, [[http://www.ismr.gatech.edu/ | 2021 International Symposium on Medical Robotics]] (ISMR) 2021. <fs>**Best Paper Award Finalist**</fs>

Here is the corresponding bibtex entry:
  @InProceedings{huang-ismr2021-LfD-tendon,
  author = {Yixuan Huang and Michael Bentley and Tucker Hermans and Alan Kuntz},
  title = {{Toward Learning Context-Dependent Tasks from Demonstration for Tendon-Driven Surgical Robots}},
  booktitle = {International Symposium on Medical Robotics (ISMR)},
  award = {Best Paper Award Finalist},
  url = {https://arxiv.org/abs/2110.07789},
  year = 2021
  }
===== Presentation =====
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