MetaMorph: Learning Universal Controllers with Transformers

@article{Gupta2022MetaMorphLU,
  title={MetaMorph: Learning Universal Controllers with Transformers},
  author={Agrim Gupta and Linxi (Jim) Fan and Surya Ganguli and Li Fei-Fei},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.11931}
}
Multiple domains like vision, natural language, and audio are witnessing tremendous progress by leveraging Transformers for large scale pre-training followed by task specific fine tuning. In contrast, in robotics we primarily train a single robot for a single task. However, modular robot systems now allow for the flexible combination of general-purpose building blocks into task optimized morphologies. However, given the exponentially large number of possible robot morphologies, training a… 

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