Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

Stimuli-responsive Novel Amphiphilic Polymers for Chemical and Biomedical Applications

Amphiphilic polymers are a class of polymers that self-assemble into different types of microstructure, depending on the solvent environment and external stimuli. Self assembly structures can exist in many different forms, such as spherical micelles, rod-like micelles, bi-layers, vesicles, bi-continuous structure etc. Most biological systems are basically comprised of many of these organised structures arranged in an intelligent manner, which impart functions and life to the system. We have adopted the atom transfer radical polymerization (ATRP) technique to synthesize various types of block copolymer systems that self-assemble into different microstructure when subject to an external stimuli, such as pH or temperature. The systems that we have studied are: (1) pH responsive fullerene (C60) containing poly(methacrylic acid) (PMAA-b-C60); (2) pH and temperature responsive fullerene containing poly[2-(dimethylamino)ethyl methacrylate] (C₆₀-b-PDMAEMA); (3) other responsive water-soluble fullerene systems. By varying temperature, pH and salt concentration, different types microstructure can be produced. In the presence of inorganic salts, fractal patterns at nano- to microscopic dimension were observed for negatively charged PMAA-b-C60, while such structure was not observed for positively charged PDMAEMA-b-C60. We demonstrated that negatively charged fullerene containing polymeric systems can serve as excellent nano-templates for the controlled growth of inorganic crystals at the nano- to micrometer length scale and the possible mechanism was proposed. The physical properties and the characteristics of their self-assembly properties will be discussed, and their implications to chemical and biomedical applications will be highlighted.