# Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery

arXiv: https://arxiv.org/abs/2204.05433 · HTML: https://mlsanity.com/paper/2204.05433/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Robot Autonomy for Surgery](https://mlsanity.com/paper/1707.03080/) (2017)

## Built on by (most cited first, 2 of 2)

- [Cross Domain Policy Transfer with Effect Cycle-Consistency](https://mlsanity.com/paper/2403.02018/) (2024)
- [Learning to Solve Tasks with Exploring Prior Behaviours](https://mlsanity.com/paper/2307.02889/) (2023)

## Abstract (arXiv)

In recent decades, the tremendous benefits surgical robots have brought to surgeons and patients have been witnessed. With the dexterous operation and the great precision, surgical robots can offer patients less recovery time and less hospital stay. However, the controls for current surgical robots in practical usage are fully carried out by surgeons via teleoperation. During the surgery process, there exists a lot of repetitive but simple manipulation, which can cause unnecessary fatigue to the surgeons. In this paper, we proposed a deep reinforcement learning-based semi-autonomous control framework for robotic surgery. The user study showed that the framework can reduce the completion time by 19.1% and the travel length by 58.7%.