# Human-Robot Shared Control for Surgical Robot Based on Context-Aware Sim-to-Real Adaptation

arXiv: https://arxiv.org/abs/2204.11116 · HTML: https://mlsanity.com/paper/2204.11116/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2022.

## Built on by (most cited first, 8 of 8)

- [Language-Guided Object-Centric Diffusion Policy for Generalizable and Collision-Aware Robotic Manipulation](https://mlsanity.com/paper/2407.00451/) (2024)
- [Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning](https://mlsanity.com/paper/2301.00452/) (2023)
- [Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot](https://mlsanity.com/paper/2307.16503/) (2023)
- [One-Shot Domain-Adaptive Imitation Learning via Progressive Learning](https://mlsanity.com/paper/2204.11251/) (2022)
- [Cross Domain Policy Transfer with Effect Cycle-Consistency](https://mlsanity.com/paper/2403.02018/) (2024)
- [Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview](https://mlsanity.com/paper/2403.19916/) (2024)
- [Deep Reinforcement Learning-Based Semi-Autonomous Control for Magnetic Micro-robot Navigation with Immersive Manipulation](https://mlsanity.com/paper/2503.06359/) (2025)
- [Hierarchical Semi-Supervised Learning Framework for Surgical Gesture Segmentation and Recognition Based on Multi-Modality Data](https://mlsanity.com/paper/2308.02529/) (2023)

## Abstract (arXiv)

Human-robot shared control, which integrates the advantages of both humans and robots, is an effective approach to facilitate efficient surgical operation. Learning from demonstration (LfD) techniques can be used to automate some of the surgical subtasks for the construction of the shared control framework. However, a sufficient amount of data is required for the robot to learn the manoeuvres. Using a surgical simulator to collect data is a less resource-demanding approach. With sim-to-real adaptation, the manoeuvres learned from a simulator can be transferred to a physical robot. To this end, we propose a sim-to-real adaptation method to construct a human-robot shared control framework for robotic surgery. In this paper, a desired trajectory is generated from a simulator using LfD method, while dynamic motion primitives (DMPs) based method is used to transfer the desired trajectory from the simulator to the physical robotic platform. Moreover, a role adaptation mechanism is developed such that the robot can adjust its role according to the surgical operation contexts predicted by a neural network model. The effectiveness of the proposed framework is validated on the da Vinci Research Kit (dVRK). Results of the user studies indicated that with the adaptive human-robot shared control framework, the path length of the remote controller, the total clutching number and the task completion time can be reduced significantly. The proposed method outperformed the traditional manual control via teleoperation.