Toward experimental validation of a model for human sensorimotor learning and control in teleoperation

Roth, Eatai; Howell, Darrin; Beckwith, Cydney; Burden, Samuel A.

doi:10.1117/12.2263844

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…where e = r − y is tracking error. Using a combination of feedback and feedforward control to model human trajectorytracking has a long history in the field [1]- [3], [5]- [7], [9], [23]- [25], and is a well-known strategy to improve performance over error feedback alone [20,Ch 8]. We emphasize, however, that certain neurologic conditions like cerebellar ataxia could impair people's ability to perform feedforward control.…”

Section: B Combined Feedback and Feedforward Improves Predictionmentioning

confidence: 99%

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami

Peterson

Howell

et al. 2023

IEEE Trans. Cybern.

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In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbancerejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward controllers. We find that performance improves because feedback adapts, regardless of the hand used. We do not detect statistically significant differences in performance or learned controllers between hands. Adaptation to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise) explains observed performance improvements.

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Section: B Combined Feedback and Feedforward Improves Predictionmentioning

confidence: 99%

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami

Peterson

Howell

et al. 2023

IEEE Trans. Cybern.

Self Cite

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show abstract

“…If a nonzero reference r ≠ 0 is known to the human, we assume that it evokes an additive feedforward F transformation of r , so that the overall human response can be written as where e = r − y is tracking error. Using a combination of feedback and feedforward control to model human trajectorytracking has a long history in the field [1]–[3], [5]–[7], [9], [23]–[25], and is a well-known strategy to improve performance over error feedback alone [20, Ch 8]. We emphasize, however, that certain neurologic conditions like cerebellar ataxia could impair people’s ability to perform feedforward control.…”

Section: Introductionmentioning

confidence: 99%

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami

Peterson

Howell

et al. 2020

Preprint

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In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a continuous trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward elements of subjects' controllers. We find that handedness does not affect the learned controller and that controllers transfer between hands. Observed improvements in time-domain tracking performance may be attributed to adaptation of feedback to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise)

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Toward experimental validation of a model for human sensorimotor learning and control in teleoperation

Cited by 2 publications

References 14 publications

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

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