text: 81,64,1506659756701749251,2235411914,Surya Ganguli,Our new #iclr2022 paper - Towards a foundation model for robotics: one transformer to control many new robot morphologies through large-scale pre-training on another set of morphologies. Expertly lead by @agrimgupta92 & collab w/@drfeifei paper: <LINK> thread -&gt; <LINK>,https://arxiv.org/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 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. MetaMorph is based on the insight that robot morphology is just another modality on which we can condition the output of a Transformer. Through extensive experiments we demonstrate that large scale pre-training on a variety of robot morphologies results in policies with combinatorial generalization capabilities, including zero shot generalization to unseen robot morphologies. We further demonstrate that our pre-trained policy can be used for sample-efficient transfer to completely new robot morphologies and tasks. ",MetaMorph: Learning Universal Controllers with Transformers,1,['Our new #iclr2022 paper - Towards a foundation model for robotics: one transformer to control many new robot morphologies through large-scale pre-training on another set of morphologies. Expertly lead by @agrimgupta92 &amp; collab w/@drfeifei paper: <LINK> thread -&gt; <LINK>'],22,03,272