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.

翻译：半自主遥操作机器人系统能够同时发挥人类与机器人的优势，实现任务的个性化执行。然而，对于自由度与传统人类操作员不同的新型软体机器人而言，尚不明确任务控制应如何在人机之间分配。本研究提出了一套人类与软体生长机器人操纵器之间的交互范式，并在真实与模拟场景中进行了验证。该机器人可通过其管状主体的外翻与内翻实现生长与收缩，我们利用这一特性实现了交互范式。我们设计并测试了六种不同的人机交互范式，从完全遥操作开始，逐步将任务执行的各个层面加入自动化。所有范式均由两名专家操作员与两名新手操作员进行演示。结果表明，人类与软体机器人操纵器能够在同时动作时沿自由度方向拆分控制权。在本研究的简单拾取与放置任务中，随着控制权逐步移交至机器人，任务性能得到提升，因为机器人能够纠正某些人类错误。然而，当任务至少部分共享时，人类的参与度与愉悦感可能达到最大化。此外，当基于软体机器人位置误差的触觉反馈辅助人类操作员时，我们观察到性能的提升高度依赖于操作员的专业水平。