Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation
This addresses the problem of scaling robotics beyond the 'one robot one task' paradigm, enabling more flexible and robust control systems, though it is incremental in combining existing techniques.
The paper tackles the challenge of learning a single controller that performs well across multiple robot morphologies by distilling diverse teacher controllers into a multi-morphology controller, achieving zero-shot generalization to unseen morphologies and robustness to damage.
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.