  Finding controllers that perform well across multiple morphologies is an
important milestone for large-scale robotics, in line with recent advances via
foundation models in other areas of machine learning. However, the challenges
of learning a single controller to control multiple morphologies make the `one
robot one task' paradigm dominant in the field. To alleviate these challenges,
we present a pipeline that: (1) leverages Quality Diversity algorithms like
MAP-Elites to create a dataset of many single-task/single-morphology teacher
controllers, then (2) distills those diverse controllers into a single
multi-morphology controller that performs well across many different body plans
by mimicking the sensory-action patterns of the teacher controllers via
supervised learning. The distilled controller scales well with the number of
teachers/morphologies and shows emergent properties. It generalizes to unseen
morphologies in a zero-shot manner, providing robustness to morphological
perturbations and instant damage recovery. Lastly, the distilled controller is
also independent of the teacher controllers -- we can distill the teacher's
knowledge into any controller model, making our approach synergistic with
architectural improvements and existing training algorithms for teacher
controllers.
