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.

Biological Routes to Gold Nanoplates

Much effort has been devoted to the synthesis of gold nanoparticles with different shapes, including the zero-dimensional nanospheres, one dimensional nanorods, and two-dimensional nanoplates. Compared to zero or one dimensional nanostructures, the synthesis of two-dimensional nanostructures in high yield has always been more involved, often requiring complex and time-consuming steps such as morphology transformation from the nanospheres, or the seeded growth process. Herein we report a high yield method for gold nanoplate synthesis using the extract of unicellular green alga Chlorella vulgaris, which can be carried out under ambient conditions. More than 90% of the total nanoparticle population is of the platelet morphology, surpassing the previously reported value of 45%. The control of the anisotropic growth of different planes; as well as the lateral size, has also been partially optimized.