Human Sensorimotor Model Adaptation to Account for Robot Accuracy and Speed Augmentation in Human-Robot Co-Manipulation
Overview
This research introduces a method for adapting human sensorimotor models within robot trajectory planning to account for both spatial-temporal task constraints and personal factors. By incorporating Fitts' law as a temporal constraint into a stochastic optimal control problem, the approach estimates parameters using recursive least squares to align robot motion duration with human behavior. The study hypothesizes that augmenting robot accuracy can lead to increased movement speed, validating this on a positioning task where the robot manages repeatable degrees of freedom while the human adapts in complementary ones. Results indicate computational suitability for online use and mutual adaptation between human and robot, though further human studies are required.
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- arXiv Robotics — research abstractsSignalHuman Sensorimotor Model Adaptation to Account for Robot Accuracy and Speed Augmentation in Human-Robot Co-Manipulation
This research introduces a method for adapting human sensorimotor models within robot trajectory planning to account for both spatial-temporal task constraints and personal factors. By incorporating Fitts' law as a temporal constraint into a stochastic optimal control problem, the approach estimates parameters using recursive least squares to align robot motion duration with human behavior. The study hypothesizes that augmenting robot accuracy can lead to increased movement speed, validating this on a positioning task where the robot manages repeatable degrees of freedom while the human adapts in complementary ones. Results indicate computational suitability for online use and mutual adaptation between human and robot, though further human studies are required.
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