Kerstin Baer and Priscy Pais
Mentor: Dr. Douglas Blank
Developmental Robotics is a relatively new approach to artificial intelligence. Instead of programming robots to perform pre-specified tasks, Developmental Robotics aims to equip them with a learning routine that allows them to make sense of their sensomotoric capabilities, discover their environment and perform self-motivated actions. Artificial neural networks have emerged as a promising learning and control mechanism. Modeled after biological neural networks, artificial networks consist of a set of interconnected units that perform calculations on a set of inputs (e.g. the sensor readings of a robot) to get some output that is appropriate for the given situation (e.g. motor commands for the robot). By manipulating the connections between units and adding more units when necessary, a neural network can adapt to new situations and learn more complex behaviors.
When a network-controlled robot is getting familiar with its environment, that is, learns to predict its environment, it has to make decisions where to move next to collect more information. Ideally the robot would pay attention to an object long enough to understand its basic features, but not so long that it forgets everything else around it. In this context we want to explore what it means for a robot to 'pay attention' to something. We intend to build an architecture around the neural network that might serve three purposes: make appropriate abstractions from the input data to make the robot “aware” of what it is paying attention to, guide the actions of the robot to keep it focused on the object of interest, and pre-process the input data to increase the network learning rate.