Inverse optimal control to model human trajectories during locomotion

Abstract

Cobotic applications require a good knowledge of human behaviour in order to be cleverly, securely and fluidly performed. For example, to make a human and a humanoid robot perform a co-navigation or a co-manipulation task, a model of human walking trajectories is essential to make the robot follow or even anticipate the human movements. This paper aims to study the Center of Mass (CoM) path during locomotion and generate human-like trajectories with an optimal control scheme. It also proposes a metric which allows to assess this model compared to the human behaviour. CoM trajectories during locomotion of 10 healthy subjects were recorded and analysed as part of this study. Inverse optimal control was used to find the optimal cost function which best fits the model to the measurements. Then, the measurements and the generated data were compared in order to assess the performance of the presented model. Even if the experiments show a great variability in human behaviours, the model presented in this study gives an accurate approximation of the average human walking trajectories. Furthermore, this model gives an approximation of human locomotion good enough to improve cobotic tasks allowing a humanoid robot to anticipate the human behaviour.

Keywords:

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

Acknowledgements

The authors would like to thank the participants who volunteered for the experiment conducted in this study as well as H. Pillet and N.A. Turpin for their useful comments to improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was funded by the French National Research Agency (ANR) as part of the project ANR-COBOT [18CE10-0003]. Study sponsor did not have a role in the study design, the data collection, analysis and interpretation, the writing of the manuscript and the decision to submit the manuscript for publication.