Abstract: Evolution sculpts both the body plans and nervous
systems of agents together over time. In contrast, in AI and
robotics, a robot’s body plan is usually designed by hand, and
control policies are then optimized for that fixed design. The task
of simultaneously co-optimizing the morphology and controller
of an embodied robot has remained a challenge. In psychology,
the theory of embodied cognition posits that behavior arises
from a close coupling between body plan and sensorimotor
control, which suggests why co-optimizing these two subsystems
is so difficult: most evolutionary changes to morphology tend
to adversely impact sensorimotor control, leading to an overall
decrease in behavioral performance. Here, we further examine
this hypothesis and demonstrate a technique for “morphological
innovation protection”, which temporarily reduces selection
pressure on recently morphologically-changed individuals, thus
enabling evolution some time to “readapt” to the new morphology
with subsequent control policy mutations. We show the potential
for this method to avoid local optima and converge to similar
highly fit morphologies across widely varying initial conditions,
while sustaining fitness improvements further into optimization.
While this technique is admittedly only the first of many steps
that must be taken to achieve scalable optimization of embodied
machines, we hope that theoretical insight into the cause of
evolutionary stagnation in current methods will help to enable
the automation of robot design and behavioral training – while
simultaneously providing a testbed to investigate the theory of
[edit database entry]
Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).