面向人机协作的情景实时编程
Situated Live Programming for Human-Robot Collaboration
摘要：
我们提出了现场编程的人机协作，一种方法，使有限的编程经验的用户编程的人机交互的协作应用程序。允许终端用户（如车间工人）自己对协作机器人进行编程，将使机器人从一个流程到另一个流程的“重新分配”变得容易，从而促进中小型企业的采用。我们的方法建立在用户操作编程（TAP）的范式之上，允许最终用户通过简单的用户操作配对来创建丰富的交互。它使最终用户能够在执行部分程序的同时迭代地创建，编辑和改进反应式机器人程序。这种实时编程方法使用户能够利用任务空间和对象，通过递增地指定位置的机器人动作对，大大降低了进入编程或重新编程机器人进行协作的障碍。我们在创作系统中实例化现场编程，用户可以通过从机器人的角度注释增强的视频馈送来创建机器人动作程序，并将机器人动作分配给触发条件。我们评估了这个系统在一项研究中，参与者（n = 10）开发的机器人程序，解决合作的轻型制造任务。结果表明，几乎没有编程经验的用户能够以互动的方式编程HRC任务，我们的现场编程方法进一步支持个性化的策略和工作流程。最后，我们讨论了所提出的方法，我们的系统实现的机会和局限性，我们的研究，并讨论了一个路线图，将这种方法扩展到更广泛的任务和应用。
英文摘要：
We present situated live programming for human-robot collaboration, an approach that enables users with limited programming experience to program collaborative applications for human-robot interaction. Allowing end users, such as shop floor workers, to program collaborative robots themselves would make it easy to "retask" robots from one process to another, facilitating their adoption by small and medium enterprises. Our approach builds on the paradigm of trigger-action programming (TAP) by allowing end users to create rich interactions through simple trigger-action pairings. It enables end users to iteratively create, edit, and refine a reactive robot program while executing partial programs. This live programming approach enables the user to utilize the task space and objects by incrementally specifying situated trigger-action pairs, substantially lowering the barrier to entry for programming or reprogramming robots for collaboration. We instantiate situated live programming in an authoring system where users can create trigger-action programs by annotating an augmented video feed from the robot's perspective and assign robot actions to trigger conditions. We evaluated this system in a study where participants (n = 10) developed robot programs for solving collaborative light-manufacturing tasks. Results showed that users with little programming experience were able to program HRC tasks in an interactive fashion and our situated live programming approach further supported individualized strategies and workflows. We conclude by discussing opportunities and limitations of the proposed approach, our system implementation, and our study and discuss a roadmap for expanding this approach to a broader range of tasks and applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
