Hand Gesture Recognition for Home Robotics

Robotics is a sector in deep ferment and constant change. The great interest that this area attracts is due to the ability of robots to carry out demanding and repetitive tasks with higher speed and precision than a human operator, a reason which has led to the strong growth in the development and adoption of large machinery belonging to the important sub-category of industrial robots. However, world society has also undergone stark changes thanks to rapid technological development in all areas, with the result that, although new lifestyles, new needs, and new problems have arisen, novel solutions that can make the life of people easier have also come to light. From this point of view, a particularly active and lively segment called Service Robotics is coming to the fore, ready to bring clear improvements mainly in contexts such as medicine, precision agriculture, logistics, security, the office, the home, and smart cities, settling down as one of the most promising emerging technological trends. The fascinating side of the development of this sector is the incessant propensity to bring robots closer to humans, making them increasingly collaborative and demonstrating over time that they can perform tasks better and better. The actual bridge between these two worlds can deservedly be represented by Artificial Intelligence, another technology that is becoming increasingly popular nowadays and allows smartly solving intricate conceptual problems characterized by complex mathematical and computer algorithms behind them. The project presented in this thesis work is an example of the union of these two cutting-edge disciplines and consists of the development of a deep learning model capable of classifying some types of dynamic hand gestures; the interpretation of the performed gesture provided as output by this model will then be used to make a wheeled robot, designed for a domestic environment, perform some specific maneuvering procedures. To achieve this result, recognizing and classifying a frame-by-frame sequence of hand landmarks coordinates, a 2D Convolutional LSTM Deep Neural Network architecture has been chosen, using a softmax layer as the output layer. The advantages offered by this solution mainly reside in the absence of communication interfaces, such as touch screens and joysticks, for controlling the robot and in the reduced amount of data to be processed by an algorithm that is also relatively light in terms of size and required computational capacity; these features allow to obtain a remarkable rapidity in classifying hand gestures and executing actions, that makes this solution combinable with other models for better usability and scalable for different contexts in which gesture recognition can be functional.