OCCUPATIONAL APPLICATIONS “Overassistive” robots can adversely impact long-term human-robot collaboration in the workplace, leading to risks of worker complacency, reduced workforce skill sets, and diminished situational awareness. Ergonomics practitioners should thus be cautious about solely targeting widely adopted metrics for improving human-robot collaboration, such as user trust and comfort. By contrast, introducing variability and adaptation into a collaborative robot’s behavior could prove vital in preventing the negative consequences of overreliance and overtrust in an autonomous partner. This work reported here explored how instilling variability into physical human-robot collaboration can have a measurably positive effect on ergonomics in a repetitive task. A review of principles related to this notion of “stimulating” robot behavior is also provided to further inform ergonomics practitioners of existing human-robot collaboration frameworks. TECHNICAL ABSTRACT Background - Collaborative robots, or cobots, are becoming ubiquitous in occupational settings due to benefits that include improved worker safety and increased productivity. Existing research on human-robot collaboration in industry has made progress in enhancing workers’ psychophysical states, by optimizing measures of ergonomics risk factors, such as human posture, comfort, and cognitive workload. However, short-term objectives for robotic assistance may conflict with the worker’s long-term preferences, needs, and overall wellbeing. Purpose - To investigate the ergonomic advantages and disadvantages of employing a collaborative robotics framework that intentionally imposes variability in the robot’s behavior to stimulate the human partner’s psychophysical state. Methods - A review of “overassistance” within human-robot collaboration and methods of addressing this phenomenon via adaptive automation. In adaptive approaches, the robot assistance may even challenge the user to better achieve a...
Imposing Motion Variability for Ergonomic Human-Robot Collaboration
Mark Zolotas
3 min read

