通过信心预测提升双手机器人缝合操作的交互效率
Confidence-based Intent Prediction for Teleoperation in Bimanual Robotic Suturing
- 用Transformer实时识别手势，动态适应操作者动作
- 基于信心值调整机器人响应，显著缩短任务时间
- 适合手术机器人交互设计与人机协同研究者
机器人辅助手术虽能提升精度，但完全自主系统受限于任务知识、非结构化环境建模与泛化能力，而全手动遥操作则存在延迟、稳定性差和感官信息不足等问题。为此，我们提出一种交互控制策略，从高、低两个层面预测操作者运动意图：高层采用基于Transformer的实时手势分类模型实现术式识别，动态适配操作行为；底层则设计基于信心值的意图融合控制器，结合用户意图与共享控制范式调节机器人动作。系统以机器人缝合任务为基础，集成传感器采集机器人运动学与任务动态数据。在不同技能水平用户上的实验表明，该方法在任务完成时间与用户满意度方面均达到统计显著提升。
原文摘要 · Abstract (English)
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.
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