We harness evolution and machine learning to have robots learn to walk, recover from damage, and adapt to unforeseen circumstances.

We investigate deep learning, which is a way to train deep neural networks (neural networks with many layers) to solve complicated tasks.

Our work shows that networks evolve to be modular when there is a cost for network connections: such modularity improves evolvability.

Creating computational evolutionary processes that--like natural evolution--endlessly produce a diversity of complex, interesting things.

Combining evolution with concepts from developmental biology produces repeated patterns in neural networks that increase intelligence.

Providing evolution with a larger palette of materials to work with leads to amazingly fun, quirky, lifelike virtual creatures.

Our work shows that we can make smarter robots by encouraging them to think differently about their environment and to try new things.

We investigate open questions in evolutionary biology with computational simulations of evolution.