A human-centric framework for robotic task learning and optimization

One of the main objectives of the fifth industrial revolution is the design and implementation of human-centric production environments. The human is, indeed, placed in the center of the production environment, having a supervision/leading role instead of carrying out heavy/repetitive tasks. To enhance such an industrial paradigm change, industrial operators have to be provided with the tools they need to naturally and easily transfer their knowledge to robotic systems. Such expertise, in fact, is difficult to be coded, especially for non-expert programmers. In addition, due to the reduced specialized manpower, the capability to transfer such knowledge into robotic systems is becoming increasingly critical and demanding. In response to this need, this contribution aims to propose and validate a human-centric approach to transfer the human's knowledge of a task into the robot controller making use of qualitative feedback only (to this end, preference-based optimization is employed). In addition, the modeled human's knowledge is exploited by an optimization algorithm (i.e., nonlinear programming) to maximize the task performance while managing the task constraints. The proposed approach has been implemented and validated for a robotic sealant material deposition task. On the basis of the qualitative feedback provided by the operator, the knowledge related to the deposition task has been transferred to the robotic system and optimized to deal with the hardware and task constraints. The achieved results show the generalization of the approach, making it possible to optimize the deposition task output.