Abstract
Robotic-assisted procedures offer enhanced precision, but while fully autonomous systems are limited in task knowledge, difficulties in modeling unstructured environments, and generalisation abilities, fully manual teleoperated systems also face challenges such as delay, stability, and reduced sensory information. To address these, we developed an interactive control strategy that assists the human operator by predicting their motion plan at both high and low levels. At the high level, a surgeme recognition system is employed through a Transformer-based real-time gesture classification model to dynamically adapt to the operator's actions, while at the low level, a Confidence-based Intention Assimilation Controller adjusts robot actions based on user intent and shared control paradigms. The system is built around a robotic suturing task, supported by sensors that capture the kinematics of the robot and task dynamics. Experiments across users with varying skill levels demonstrated the effectiveness of the proposed approach, showing statistically significant improvements in task completion time and user satisfaction compared to traditional teleoperation.
Abstract (translated)
机器人辅助程序提供了更高的精确度，但完全自主系统在任务知识、模拟非结构化环境的难度以及泛化能力方面存在局限。与此同时，全手动遥操作系统也面临着延迟、稳定性降低和感觉信息减少等问题。为解决这些问题，我们开发了一种互动控制策略，该策略通过预测操作员的动作计划，在高层次和低层次上辅助人类操作员。 在高层次上，采用基于Transformer的实时手势分类模型来识别手术动作（surgeme），使系统能够根据操作者的行动动态适应。在低层次上，一种基于信心度的意图融合控制器会根据用户意图及共享控制范式调整机器人的行为。 该系统围绕一个机器人缝合任务构建，并利用传感器捕捉机器人的运动学和任务动力学。经过不同技能水平用户的实验测试，结果显示所提出的方法在任务完成时间和用户满意度方面相比传统遥操作有统计显著的改进。
URL
https://arxiv.org/abs/2504.20761
