MetaMorph: Learning Universal Controllers with Transformers
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MetaMorph: Learning Universal Controllers with Transformers
Agrim Gupta,
Linxi Fan,
Surya Ganguli,
Fei-Fei Li
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 controller for each new design is impractical. In this work, we propose MetaMorph, a Transformer based approach to learn a universal controller over a modular robot design space.