Semi-autonomous telerobotic systems allow both humans and robots to exploit their strengths, while enabling personalized execution of a task. However, for new soft robots with degrees of freedom dissimilar to those of human operators, it is unknown how the control of a task should be divided between the human and robot. This work presents a set of interaction paradigms between a human and a soft growing robot manipulator, and demonstrates them in both real and simulated scenarios. The robot can grow and retract by eversion and inversion of its tubular body, a property we exploit to implement interaction paradigms. We implemented and tested six different paradigms of human-robot interaction, beginning with full teleoperation and gradually adding automation to various aspects of the task execution. All paradigms were demonstrated by two expert and two naive operators. Results show that humans and the soft robot manipulator can split control along degrees of freedom while acting simultaneously. In the simple pick-and-place task studied in this work, performance improves as the control is gradually given to the robot, because the robot can correct certain human errors. However, human engagement and enjoyment may be maximized when the task is at least partially shared. Finally, when the human operator is assisted by haptic feedback based on soft robot position errors, we observed that the improvement in performance is highly dependent on the expertise of the human operator.

中文翻译：

---

软生长机器人机械手的共享控制遥操作范式

半自主遥控机器人系统允许人类和机器人发挥各自的优势，同时实现任务的个性化执行。然而，对于自由度与人类操作员不同的新型软机器人，尚不清楚如何在人与机器人之间分配任务的控制。这项工作提出了人类和软增长机器人操纵器之间的一组交互范式，并在真实和模拟场景中进行了演示。机器人可以通过其管状体的外翻和倒转来生长和缩回，我们利用这一特性来实现交互范式。我们实施并测试了六种不同的人机交互范式，从完全远程操作开始，逐渐将自动化添加到任务执行的各个方面。所有范式都由两个专家和两个幼稚的操作员演示。结果表明，人类和软机器人机械手可以在同时动作的同时沿自由度拆分控制。在这项工作中研究的简单拾放任务中，随着控制逐渐交给机器人，性能得到提高，因为机器人可以纠正某些人为错误。然而，当至少部分地共享任务时，人类参与和享受可以最大化。最后，当人类操作员通过基于软机器人位置误差的触觉反馈辅助时，我们观察到性能的提高高度依赖于人类操作员的专业知识。在这项工作中研究的简单拾放任务中，随着控制逐渐交给机器人，性能得到提高，因为机器人可以纠正某些人为错误。然而，当至少部分地共享任务时，人类参与和享受可以最大化。最后，当人类操作员通过基于软机器人位置误差的触觉反馈辅助时，我们观察到性能的提高高度依赖于人类操作员的专业知识。在这项工作中研究的简单拾放任务中，随着控制逐渐交给机器人，性能得到提高，因为机器人可以纠正某些人为错误。然而，当至少部分地共享任务时，人类参与和享受可以最大化。最后，当人类操作员通过基于软机器人位置误差的触觉反馈辅助时，我们观察到性能的提高高度依赖于人类操作员的专业知识。