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.

通过使用 Quality Diversity 算法构建多个单任务 / 单形态的教师控制器的数据集，然后通过监督学习将这些多样化控制器提炼成一个能够在多种不同机体中表现良好的单一多形态控制器，本文提出了一种解决学习单一控制器控制多种形态的挑战的流程。这种提炼控制器的方法在教师控制器的知识展现方面是独立的，使得我们能够将教师的知识提炼到任何控制模型中，使我们的方法与架构改进和现有的教师控制器训练算法具有协同效应。