Enabling physical human-robot collaboration through contact classification and reaction

In this paper, a scenario of physical human-robot collaboration is considered, in which a robot is able to both carry out autonomous tasks and to physically interact with a human operator to achieve a common objective. However, since human and robot share the same workspace both accidental and intentional contacts between them might arise. Therefore, a solution based on Recurrent Neural Networks (RNNs) is proposed to detect and classify the nature of the contact with the human, even in the case the robot is interacting with the environment because of its own task. Then, reaction strategies are defined depending on the nature of contact: human avoidance with evasive action in the case of accidental interaction, and admittance control in the case of intentional interaction. In regard to the latter, Control Barrier Functions (CBFs) are considered to guarantee the satisfaction of robot constraints, while endowing the robot with a compatible compliant behavior. The approach is validated on real data acquired from the interaction with a Kinova Jaco2.