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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

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Published on: August 15, 2020

Inverse optimal control to model human trajectories during locomotion.

Isabelle Maroger¹, Olivier Stasse¹, Bruno Watier¹

¹LAAS-CNRS, Université de Toulouse, CNRS, UPS, Toulouse, France.

Computer Methods in Biomechanics and Biomedical Engineering

|August 16, 2021

Summary

This summary is machine-generated.

This study models human walking trajectories using optimal control to enhance cobotic tasks. The generated human-like paths allow robots to anticipate and fluidly interact with human movements.

Keywords:

Locomotion analysis human-robot interaction model-based simulation modeling optimal control

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Area of Science:

Robotics
Biomechanics
Human-Robot Interaction

Background:

Cobotic applications necessitate understanding human behavior for seamless interaction.
Humanoid robots require accurate models of human movement for tasks like co-navigation and co-manipulation.

Purpose of the Study:

To model human Center of Mass (CoM) trajectories during locomotion.
To generate human-like trajectories using an optimal control scheme.
To propose a metric for assessing model accuracy against human behavior.

Main Methods:

Recorded and analyzed CoM trajectories of 10 healthy subjects.
Employed inverse optimal control to identify the optimal cost function.
Generated human-like trajectories and compared them to recorded data.

Main Results:

The developed model provides an accurate approximation of average human walking trajectories.
Despite natural variability in human gaits, the model captures essential movement patterns.
The model's accuracy is sufficient for improving cobotic task performance.

Conclusions:

The study successfully models human locomotion CoM paths for robotic applications.
The generated trajectories enable humanoid robots to anticipate human movements.
This research advances the fluidity and safety of human-robot collaborative tasks.