Dr. Andrew Blake, Senior Research Scientist and leader of the vision group at Microsoft Research Cambridge, is one the world's leading experts in image interaction, stereo vision and motion tracking.

Modern probabilistic modelling has revolutionized the design and implementation of machine vision systems. There are now numerous instances of systems that can see stereoscopically in depth, or separate foreground from background, or accurately excise objects of a particular class, all in real time. Each of those three vision functionalities will be demonstrated in the lecture. The underlying advances in system design and performance owe much to probabilistic frameworks for inference in images. In particular, the Markov Random Field (MRF) , which first appeared in image processing in the 70s, has staged a resounding comeback in the last decade. The MRF is a mechanism, borrowed from statistical physics, for expressing prior properties of images, such as smoothness and spatial coherence. Despite its considerable generality, the MRF has proved nonetheless to be remarkably tractable when used in inference systems, as the lecture will explain.

Dr. Naomi Leonard, Professor of Mechanical and Aerospace Engineering at Princeton University, is world renowed for her work in nonlinear control theory, geometric mechanics, and cooperative control.

From bird flocks to fish schools, animals move together and respond to their environment in remarkable ways; their natural collective motion patterns appear well choreographed and their collective survival strategies seem ingenious. Animal group behaviors inspire design for mobile multi-agent robotic systems, where demanding cooperative sensing tasks, such as exploration and sampling in an uncertain and dynamic environment, find their analogue in natural aggregation behaviors, such as foraging and feeding. However, bio-inspiration of this kind is not transparent because the natural design mechanisms are not well understood. The joint challenge is to explain the enabling mechanisms in animal groups and to define provable mechanims for robotic groups. And this suggests an integrated approach: formal bio-inspired models and analysis tools derived to synthesize robotic motion and exploration can be used to evaluate design hypotheses for animal groups; subsequent revelations from the biology will in turn inspire new approaches for robotic systems. I will discuss mobile robot and animal networks using a common mathematical framework that builds on coupled oscillator dynamics and communication graphs. I will describe application to an adaptive ocean sampling network, a successful, recent field experiment in Monterey Bay, CA and an investigation of dynamics and decision-making in fish schools.

Brain-controlled Robots
Friday, May 23, 2008 (1100-1200)

Ten years have passed since the Japanese 'Century of the Brain' was promoted, and its most notable objective, the unique 'Creating the Brain' approach, has led us to apply a humanoid robot as a neuroscience tool. Here, we aim to understand the brain to the extent that we can make humanoid robots solve tasks typically solved by the human brain by using essentially the same principles. I postulate that this 'Understanding the Brain by Creating the Brain' approach is the only way to fully understand neural mechanisms in a rigorous sense. Even if we could create an artificial brain, we could not investigate its functions, such as vision or motor control, if we just let it float in incubation fluid in a jar. The brain must be connected to sensors and a motor apparatus so that it can interact with its environment. A humanoid robot controlled by an artificial brain, which is implemented as software based on computational models of brain functions, seems to be the most plausible candidate for this purpose, given currently available technology. With the slogan of 'Understanding the Brain by Creating the Brain', in the mid-80s we started to use robots for brain research (Miyamoto & Kawato 1988), and about 10 different kinds of robots have been used by our group at Osaka University's Department of Biophysical Engineering, ATR Laboratories, ERATO Kawato Dynamic Brain Project (ERATO 1996-2001), and ICORP Kawato Computational Brain Project (ICOPR 2004-2009).