  The recognition of actions performed by humans and the anticipation of their
intentions are important enablers to yield sociable and successful
collaboration in human-robot teams. Meanwhile, robots should have the capacity
to deal with multiple objectives and constraints, arising from the
collaborative task or the human. In this regard, we propose vision techniques
to perform human action recognition and image classification, which are
integrated into an Augmented Hierarchical Quadratic Programming (AHQP) scheme
to hierarchically optimize the robot's reactive behavior and human ergonomics.
The proposed framework allows one to intuitively command the robot in space
while a task is being executed. The experiments confirm increased human
ergonomics and usability, which are fundamental parameters for reducing
musculoskeletal diseases and increasing trust in automation.
