Projects
Pyro Robotics: The goal of the project is to provide a programming environment for easily exploring advanced topics in artificial intelligence and robotics without having to worry about the low-level details of the underlying hardware. That is not to say that Pyro is just a toy. In fact, Pyro is used for real robotics research as well as courseware. Pyro can be found at PyroRobotics.org. Pyro wins 2005 NEEDS award!
Bubble, the robot blimp: Bubble is a fully-autonomous robotic blimp controlled via Pyro. Bubble won second prize in the second annual Indoor Aerial Robot Competition in 2006.
The Role of Social Interaction in Developmental Robotics: Julia Ferraioli, Leslie McTavish and George Dahl under the supervision of Professor Doug Blank. Abstract:
Developmental robotics involves designing learning systems for robots such that they are capable of displaying sophisticated behaviors that have not been directly programmed into them. Traditionally, robots can only do what we program them to do. They are not capable of acquiring any new information and therefore are not able to adapt to new situations. Because of the limitations of this traditional approach, new methods are being explored.
The developmental approach to learning attempts to create a models that mimic human learning development. The robot should first develop a sense of its self and its environment by detecting and establishing a relationship between its sensor and motor values. We will try to do this by experimenting with combinations of various established learning algorithms.
Learning algorithms are methods by which the system autonomously makes generalizations about input which is provided to it. These algorithms allow the system to learn a task or task without explicit programming or memorization. To achieve our goal we will design developmental algorithms which incorporate several learning processes such as neural networks, self-organizing maps (SOMs), resource allocating vector quantizers (RAVQs), growing neural gas (GNG) as well as others.
In the next stage of development, the robots will be assisted by social interactions with other robots or human guidance. We believe social interaction is an important aspect of human learning and may also prove to be useful in developing intelligent robots. Our experiments will test the effect of social interaction in the developmental process. We hope to design a developmental learning system that can be applied to both simulated and real robots and is independent of environment and robot type.
Cascade Correlation: Implementing cascade-correlation in Python.
