Human Sensorimotor Model Adaptation to Account for Robot Accuracy and Speed Augmentation in Human-Robot Co-Manipulation
Human 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.
The paper proposes a stochastic optimal control framework that adapts robot trajectory planning to human sensorimotor constraints using Fitts' law parameters estimated via recursive least squares, validated on a positioning task where the robot controls repeatable degrees of freedom and the human adapts complementary ones.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2611.02181v1 Announce Type: new Abstract: In human-robot co-manipulation, the robot's trajectory should accommodate the human's, where human sensorimotor behavior is affected by the task's spatial and temporal constraints, as well as personal factors. Several robot trajectory planning methods have been proposed to embed human sensorimotor models in the robot trajectory planning, but these methods do not conside
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hat of the human, thus allowing the robot to adapt to how the human behaves when interacting with the robot. It is hypothesized that a speed increase can result from the robot augmenting the accuracy. The feasibility of the approach is validated on a positioning task where the robot controls degrees of freedom which have repeatable motion, and the human adapts motion in the complementary degrees of freedom. The appro
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ach is shown to be computationally suited to online use, it converges as the human and robot mutually adapt, and can increase the movement speed, although thorough human studies are needed in future work.
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Source：arXiv Robotics — research abstracts · arxiv.org