Many important mechanical properties can only be measured using physical experiments, which means that design for such properties happens through a slow and expensive iterative cycle. Here, we describe our efforts to reinvent this paradigm using autonomous experimentation. In particular, we combine robots to perform experiments in an automated and reliable fashion with machine learning to select each subsequent design. Benchmarking has revealed that this process converges on high performing structures nearly 60 times faster than conventional grid searching. We discuss how autonomous experimentation accelerates the design process and allow us to identify tough and resilient structures for numerous applications ranging from personal protective equipment to crumple zones on cars.