Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control
<|reference_start|>Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control: Learning a universal policy across different robot morphologies can significantly improve learning efficiency and enable zero-shot generalization to unseen morphologies. However, learning a highly performant universal policy requires sophisticated architectures like transformers (TF) that have larger memory and computational cost than simpler multi-layer perceptrons (MLP). To achieve both good performance like TF and high efficiency like MLP at inference time, we propose HyperDistill, which consists of: (1) A morphology-conditioned hypernetwork (HN) that generates robot-wise MLP policies, and (2) A policy distillation approach that is essential for successful training. We show that on UNIMAL, a benchmark with hundreds of diverse morphologies, HyperDistill performs as well as a universal TF teacher policy on both training and unseen test robots, but reduces model size by 6-14 times, and computational cost by 67-160 times in different environments. Our analysis attributes the efficiency advantage of HyperDistill at inference time to knowledge decoupling, i.e., the ability to decouple inter-task and intra-task knowledge, a general principle that could also be applied to improve inference efficiency in other domains.<|reference_end|>
@article{xiong2024distilling,
    title={Distilling Morphology-Conditioned Hypernetworks for Efficient Universal
  Morphology Control},
    author={Zheng Xiong, Risto Vuorio, Jacob Beck, Matthieu Zimmer, Kun Shao,
  Shimon Whiteson},
    journal={arXiv preprint arXiv:2402.06570},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06570},
    primaryClass={cs.LG cs.RO}
}