In this paper, we present a novel learning-based shared control framework. This framework deploys first-order Dynamical Systems (DS) as motion generators providing the desired reference motion, and a Variable Stiffness Dynamical Systems (VSDS) \cite{chen2021closed} for haptic guidance. We show how to shape several features of our controller in order to achieve authority allocation, local motion refinement, in addition to the inherent ability of the controller to automatically synchronize with the human state during joint task execution. We validate our approach in a teleoperated task scenario, where we also showcase the ability of our framework to deal with situations that require updating task knowledge due to possible changes in the task scenario, or changes in the environment. Finally, we conduct a user study to compare the performance of our VSDS controller for guidance generation to two state-of-the-art controllers in a target reaching task. The result shows that our VSDS controller has the highest successful rate of task execution among all conditions. Besides, our VSDS controller helps reduce the execution time and task load significantly, and was selected as the most favorable controller by participants.

翻译：本文提出一种新颖的基于学习的共享控制框架。该框架采用一阶动力系统作为运动生成器以提供期望参考运动，并利用变刚度动力系统实现触觉引导。我们展示了如何设计控制器的多个特征，以实现权威分配、局部运动优化，以及控制器在联合任务执行过程中自动同步人类状态的固有能力。我们在远程操作任务场景中验证了该方法，同时展示了框架应对因任务情景变化或环境改变而需更新任务知识场景的能力。最后，我们开展用户研究，在目标到达任务中将本VSDS控制器的引导生成性能与两种最新控制器进行对比。结果表明，本VSDS控制器在所有条件下任务执行成功率最高，且显著缩短执行时间并降低任务负荷，被参与者评为最受青睐的控制器。