An important component for the effective collaboration
of humans with robots is the compatibility of their
movements, especially when humans physically collaborate with
a robot partner. Following previous findings that humans
interact more seamlessly with a robot that moves with humanlike
or biological velocity profiles, this study examined whether
humans can adapt to a robot that violates human signatures.
The specific focus was on the role of extensive practice and realtime
augmented feedback. Six groups of participants physically
tracked a robot tracing an ellipse with profiles where velocity
scaled with the curvature of the path in biological and nonbiological
ways, while instructed to minimize the interaction
force with the robot. Three of the 6 groups received real-time
visual feedback about their force error. Results showed that
with 3 daily practice sessions, when given feedback about their
force errors, humans could decrease their interaction forces
when the robot’s trajectory violated human-like velocity patterns.
Conversely, when augmented feedback was not provided,
there were no improvements despite this extensive practice. The
biological profile showed no improvements, even with feedback,
indicating that the (non-zero) force had already reached a
floor level. These findings highlight the importance of biological
robot trajectories and augmented feedback to guide humans to
adapt to non-biological movements in physical human-robot
interaction. These results have implications on various fields of
robotics, such as surgical applications and collaborative robots
for industry.
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Separating neural influences from peripheral mechanics: the speed-curvature relation in mechanically constrained actions
While the study of unconstrained movements has revealed important features of neural control, generalizing those insights to more sophisticated object manipulation is challenging. Humans excel at physical interaction with objects, even when those objects introduce complex dynamics and kinematic constraints. This study examined humans turning a horizontal planar crank (radius 10.29 cm) at their preferred and three instructed speeds (with visual feedback), both in clockwise and counterclockwise directions. To explore the role of neuromechanical dynamics, the instructed speeds covered a wide range: fast (near the limits of performance), medium (near preferred speed), and very slow (rendering dynamic effects negligible). Because kinematically constrained movements involve significant physical interaction, disentangling neural control from the influences of biomechanics presents a challenge. To address it, we modeled the interactive dynamics to “subtract off” peripheral biomechanics from observed force and kinematic data, thereby estimating aspects of underlying neural action that may be expressed in terms of motion. We demonstrate the value of this method: remarkably, an approximately elliptical path emerged, and speed minima coincided with curvature maxima, similar to what is seen in unconstrained movements, even though the hand moved at nearly constant speed along a constant-curvature path. These findings suggest that the neural controller takes advantage of peripheral biomechanics to simplify physical interaction. As a result, patterns seen in unconstrained movements persist even when physical interaction prevents their expression in hand kinematics. The reemergence of a speed-curvature relation indicates that it is due, at least in part, to neural processes that emphasize smoothness and predictability. NEW & NOTEWORTHY Physically interacting with kinematic constraints is commonplace in everyday actions. We report a study of humans turning a crank, a circular constraint that imposes constant hand path curvature and hence should suppress variations of hand speed due to the power-law speed-curvature relation widely reported for unconstrained motions. Remarkably, we found that, when peripheral biomechanical factors are removed, a speed-curvature relation reemerges, indicating that it is, at least in part, of neural origin.
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- NSF-PAR ID:
- 10189505
- Date Published:
- Journal Name:
- Journal of Neurophysiology
- Volume:
- 123
- Issue:
- 5
- ISSN:
- 0022-3077
- Page Range / eLocation ID:
- 1870 to 1885
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1152/jn.00536.2019
