Biological systems, from multicellular organisms to
social insects ("superorganisms"), get tremendous mileage from the
cooperation of vast numbers of cheap, unreliable, and limited
individuals. As we build embedded systems with similar characteristics
--- modular robots, robot swarms, sensor networks, programmable
materials --- can we achieve the kind of complexity and reliability
that nature achieves?
Our group is interested in self-organizing multi-agent systems, where large
numbers of simple agents cooperate to produce complex and robust
global behavior. We study bio-inspired programming paradigms for
designing collective intelligence in robotics and sensor-actuator
networks, drawing inspiration mainly from multicellular biology and
social insects. We also investigate models of self-organization in
biology, specifically how cells cooperate during the development of
multicellular organisms. A common theme in all of our work is understanding the relationship between local and global
behavior: how does robust collective behavior arise from
many locally interacting agents, and how can we program the local
interations of simple agents to achieve the global behaviors we
want.
We work on three main areas:
Bio-inspired Multi-agent Models and
Theory
We explore
artificial multi-agent models inspired by
self-organising and self-repairing behavior in
biology. We are especially interested in
global-to-local compilation and theory,
i.e. how user-specified global goals can be translated
into local agent interactions and how one can reason
about the correctness and complexity of agent
rules. Our goal is to show how biological design
principles can be formally captured, generalized to
new tasks, and theoretically analyzed.
Bio-inspired Multi-agent Systems in Robotics
and Sensor Networks
We
study bio-inspired approaches for programming embedded
systems that rely on large numbers of relatively cheap
and simple agents, e.g. reconfigurable modular
robots, swarm robotics, and sensor networks. We
design, analyze, and implement decentralized
algorithms and use these as the basis for
global-to-local compilers that provably achieve wide
classes of user-specified global goals. We also build
prototype robot systems using inspiration from
biology -- e.g. self-adapting modular robots and
insect-inspired mobile robots -- that implement the
algorithmic ideas.
Multi-cellular Systems Biology
We develop mathematical and
computational models of cell behavior to investigate
how system-level properties emerge in multicellular
development. Our goal is to elucidate the relationship
between local cell programs and global
tissue-level outcomes during development and
disease. This work is in close collaboration with
experimental biologists, and most of our current work
is focused on epithelial tissues and fruit fly
development.
