发表时间:NeurIPS 2024
论文链接:https://readpaper.com/pdf-annotate/note?pdfId=2517084963921814272¬eId=2519032879246930432
作者单位:MIT CSAIL(Kaiming开始做具身了)
Motivation:Previous robot learning methods often collect data to train with one specific embodiment for one task(不够通用), which is expensive and prone to overfitting.

解决方法:This work studies the problem of learning policy representations through heterogeneous pretraining on robot data across different embodiments and tasks at scale.
实现方式:提出Heterogeneous Pre-trained Transformers (HPT), which pre-train a large, shareable trunk of a policy neural network to learn a task and embodim
用异构预训练Transformer扩展本体视觉学习

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