Tiago Pereira do Nascimento presents Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains

In HRI, complex command (action) classification is still an open problem that
usually prevents the real applicability of such a technique. The literature
presents some works that use neural networks to detect these actions. However,
occlusion is still a major issue in HRI, especially when using uncrewed aerial
vehicles (UAVs), since, during the robot's movement, the human operator is
often
out of the robot's field of view. Furthermore, in multi-robot scenarios,
distributed training is also an open problem. In this sense, this semimar will
present an action recognition and control approach based on Long Short-Term
Memory (LSTM) Deep Neural Networks with two layers in association with three
densely connected layers and Federated Learning (FL) embedded in multiple
drones. The FL enabled our approach to be trained in a distributed fashion,
i.e., access to data without the need for cloud or other repositories, which
facilitates the multi-robot system's learning. This seminar will be short and
20
minutes long, following questions.