Mentor: Dr. Doug Blank
Developmental robotics is a interdisciplinary field of study working towards understanding the human mind, and emulating such complex processes in mathematical computations done by a computer chip. In doing so developmental roboticists must give the robot’s mind the ability to learn in real-time, on its own, and to develop its own goals and motivations in time based on what it has learned. Many existing artificial neural networks (ANN; mathematical representations of a human neural network) cannot handle real-time input, or have poor memory handling, causing the networks to forget what they have learned once the robot travels to a different environment, and to be unable to effectively process new input. Thus the purpose of this experiment is to explore the many different structures for ANNs, and to modify them to create a better learning system for robots. By improving the way the ANN retains memory, how it reacts to different stimuli, and how it makes generalizations and abstractions of its environment, we hope to work towards developing a network that can learn effectively as it explores its environment.