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Objective:The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort.Background:The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human–robot interaction.Method:We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires.Results:When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot.Conclusion:People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human–robot team fluency and human worker satisfaction.Application:Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human–robot collaboration.

Ensuring human safety is one of the most important considerations within the field of human-robot interaction (HRI). This does not simply involve preventing collisions between humans and robots operating within a shared space; we must consider all possible ways in which harm could come to a person, ranging from physical contact to adverse psychological effects resulting from unpleasant or dangerous interaction. In this work, we define what safe HRI entails and present a survey of potential methods of ensuring safety during HRI. We classify this collection of work into four major categories: safety through control, motion planning, prediction, and consideration of psychological factors. We discuss recent work in each major category, identify various sub-categories and discuss how these methods can be utilized to improve HRI safety. We then discuss gaps in the current literature and suggest future directions for additional work. By creating an organized categorization of the field, we hope to support future research and the development of new technologies for safe HRI, as well as facilitate the use of these techniques by researchers within the HRI community.

Abstract-Allowing humans and robots to interact in close proximity to each other has great potential for increasing the effectiveness of human-robot teams across a large variety of domains. However, as we move toward enabling humans and robots to interact at ever-decreasing distances of separation, effective safety technologies must also be developed. While new, inherently human-safe robot designs have been established, millions of industrial robots are already deployed worldwide, which makes it attractive to develop technologies that can turn these standard industrial robots into human-safe platforms. In this work, we present a real-time safety system capable of allowing safe human-robot interaction at very low distances of separation, without the need for robot hardware modification or replacement. By leveraging known robot joint angle values and accurate measurements of human positioning in the workspace, we can achieve precise robot speed adjustment by utilizing real-time measurements of separation distance. This, in turn, allows for collision prevention in a manner comfortable for the human user. We demonstrate our system achieves latencies below 9.64 ms with 95% probability, 11.10 ms with 99% probability, and 14.08 ms with 99.99% probability, resulting in robust real-time performance.

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