Walking paths during collaborative carriages do not follow the simple rules observed in the locomotion of single walking subjects.

Some works have already studied human trajectories during spontaneous locomotion. However, this topic has not been thoroughly studied in the context of human-human interactions, especially during collaborative carriage tasks. Thus, this manuscript aims to provide a broad analysis of the kinematics of two subjects carrying a table. In the present study, 20 pairs of subjects moved a table to 9 different goal positions distant of 2.7-5.4 m. This was performed with only one or both subjects knowing the target location. The analysis of the collected data demonstrated that there is no shared strategy implemented by all the pairs to move the table around. We observed a great variability in the pairs' behaviours. Even the same pair can implement various strategies to move a table to the same goal position. Moreover, a model of the trajectories adopted by collaborating pairs was proposed and optimized with an inverse optimal control scheme. Even if it produced consistent results, due to the great variability which origins were not elucidated, it was not possible to accurately simulate the average trajectories nor the individual ones. Thus, the approach that was shown to be efficient to simulate single walking subjects failed to model the behaviour of collaborating pairs.

Inverse optimal control to model human trajectories during locomotion.

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.