Designing modular robots that employ heterogeneous modules aligned with a grid can be accomplished via search over the occupancy matrix that specifies the module type in each grid cell. A generative encoding (NEAT) creates a network that yields the module type in each cell when queried with the cell's grid position. Each individual (simulated) robot created in this way participates in an evolutionary competition to pass on parts of its generative encoding to the next generation, with faster individuals more likely to pass on their genes. This approach allows the high-dimensional design space to be explored and yields many qualitatively unique solutions that all move quickly - an approach that aids design discovery.

The design of soft robots is primarily undertaken by teams of human designers, and their fabrication relies on traditional manufacturing processes like silicone casting.

We explore the fabrication of print-in-place soft robots by additive manufacturing. Advanced additive manufacturing eliminates time-consuming manual fabrication steps as well as fabrication constraints inherent in traditional fabrication processes.

Additionally, we discover novel soft robot designs through automated, algorithm-driven processes, which are capable of much broader searches through the rugged design terrain characteristic of the soft robotics field. We envision automated design tools which extend the reach of human design teams, allowing them to more rapidly synthesize complex soft robot designs.