Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation
<|reference_start|>Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation: 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.<|reference_end|>
@article{mertan2024towards,
    title={Towards Multi-Morphology Controllers with Diversity and Knowledge
  Distillation},
    author={Alican Mertan and Nick Cheney},
    journal={arXiv preprint arXiv:2404.14625},
    year={2024},
    doi={10.1145/3638529.3654013},
    archivePrefix={arXiv},
    eprint={2404.14625},
    primaryClass={cs.RO cs.LG cs.NE}
}