Imposing Motion Variability for Ergonomic Human-Robot Collaboration.

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.

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 long-term objective while partially conflicting with their short-term task goals. Common themes across these approaches were extracted to motivate and support the proposed idea of stimulating robot behavior in physical human-robot collaboration.Results: Experimental evidence to justify stimulating robot behavior is presented through a human-robot handover study. A robot handover policy that regularly injects variability into the object transfer location led to significantly larger dynamics in the torso rotations and center of mass of human receivers compared to an "overassistive" policy that constrains receiver motion. Crucially, the stimulating handover policy also generated improvements in widely used ergonomics risk indicators of human posture.Conclusions: Our findings underscore the potential ergonomic benefits of a cobot's actions imposing variability in a user's responsive behavior, rather than indirectly restricting human behavior by optimizing the immediate task objective. Therefore, a transition from cobot policies that optimize instantaneous measures of ergonomics to those that continuously engage users could hold promise for human-robot collaboration in occupational settings characterized by repeated interactions.

Motion Polytopes in Virtual Reality for Shared Control in Remote Manipulation Applications.

In remote applications that mandate human supervision, shared control can prove vital by establishing a harmonious balance between the high-level cognition of a user and the low-level autonomy of a robot. Though in practice, achieving this balance is a challenging endeavor that largely depends on whether the operator effectively interprets the underlying shared control. Inspired by recent works on using immersive technologies to expose the internal shared control, we develop a virtual reality system to visually guide human-in-the-loop manipulation. Our implementation of shared control teleoperation employs end effector manipulability polytopes, which are geometrical constructs that embed joint limit and environmental constraints. These constructs capture a holistic view of the constrained manipulator's motion and can thus be visually represented as feedback for users on their operable space of movement. To assess the efficacy of our proposed approach, we consider a teleoperation task where users manipulate a screwdriver attached to a robotic arm's end effector. A pilot study with prospective operators is first conducted to discern which graphical cues and virtual reality setup are most preferable. Feedback from this study informs the final design of our virtual reality system, which is subsequently evaluated in the actual screwdriver teleoperation experiment. Our experimental findings support the utility of using polytopes for shared control teleoperation, but hint at the need for longer-term studies to garner their full benefits as virtual guides.