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The recognition of actions performed by humans and the anticipation of theirintentions are important enablers to yield sociable and successfulcollaboration in human-robot teams. Meanwhile, robots should have the capacityto deal with multiple objectives and constraints, arising from thecollaborative task or the human. In this regard, we propose vision techniquesto perform human action recognition and image classification, which areintegrated into an Augmented Hierarchical Quadratic Programming (AHQP) schemeto hierarchically optimize the robot's reactive behavior and human ergonomics.The proposed framework allows one to intuitively command the robot in spacewhile a task is being executed. The experiments confirm increased humanergonomics and usability, which are fundamental parameters for reducingmusculoskeletal diseases and increasing trust in automation.

Sociable and Ergonomic Human-Robot Collaboration through Action Recognition and Augmented Hierarchical Quadratic Programming