Purpose
The vision of this project is that the learning of the optimal local control policies can be substantially accelerated by sharing both information about previous experiences and computation across multiple neighbour Cobots connected through a wireless communication network, providing solutions that satisfy the necessary real-time constraints in the considered robotic applications, as well as providing sufficient robustness and interchangeability to the control solutions.
This multidisciplinary project covers the areas of deep learning, optimisation, reinforcement learning, decentralised shared control, embodied Artificial Intelligence (intelligent robots and devices), bi-directional interaction between Cobots and human operators, and cross-layer networking with a significant potential in industrial applications.
Project description
In recent years, collaborative robot systems have received a great deal of attention in the context of a large number of industrial applications, such as additive manufacturing, automotive manufacturing, material handling, packaging and co-packing and quality inspection. The collaboration between multiple collaborative robots and human operators is considered to be the most prominent strategy in Industry 4.0 and future Industry 5.0, sharing the same space and collaborating on tasks according to their complementary capabilities.
DEEPCOBOT project will investigate the design of a new generation of decentralized data-driven Deep Learning based controllers for multiple coexisting collaborative robots (Cobots), which interact both between themselves and with human operators in order to collectively learn from each other's experiences and perform cooperatively different complex tasks in large-scale industrial environments. This is motivated by the increasing demand of automation in industry, especially the demand of a safer and more efficient collaboration between multiple Cobots and human operators to integrate the best of human abilities and robotic automation.
