Body Gesture Recognition to Control a Social Robot
This work addresses the need for improved social robotics applications, such as human-robot collaboration, though it appears incremental by applying neural networks to a custom dataset.
The authors tackled the problem of enabling natural human-robot interaction by proposing a gesture-based language, achieving remarkable results validated in simulations and real-life experiments with non-trained volunteers.
In this work, we propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neural networks and a custom dataset of humans performing a set of body gestures to train our network. Furthermore, we compare body gesture communication with other communication channels to acknowledge the importance of adding this knowledge to robots. The presented approach is extensively validated in diverse simulations and real-life experiments with non-trained volunteers. This attains remarkable results and shows that it is a valuable framework for social robotics applications, such as human robot collaboration or human-robot interaction.