Leader-follower roles and dynamic features

The objective is to extract leader-follower roles and dynamic features such as configuration-dependent stiffness (CDS) from videos of humans performing collaborative tasks (here, a two-person wood sawing), and replicating them in a dual-arm robotic setup. We created a dataset that is not dispersive in its classes but sectoral, i.e., dedicated exclusively to the industrial environment and human-robot collaboration. Specifically, we described our ongoing collection of the ’HRI30’ database for industrial action recognition from videos, containing 30 categories of industrial-like actions and 2940 manually annotated clips. The goal was to propose vision techniques for human action recognition and image classification, integrated into an augmented hierarchical quadratic programming (AHQP) scheme to hierarchically optimize the robot’s reactive behavior and human ergonomics. This framework allows the robot to be intuitively commanded in space while performing a task.