JEDI: Joint-Embedding Demonstration Compression for In-Context Imitation
Overview
This research proposes JEDI, a method that compresses demonstrations into latent slots trained to predict future keyframes in a frozen video-JEPA space. Task and process counterfactuals help preserve execution details, and experiments on LIBERO and real robots show improved success and demonstration following over action-only compression.
From arXiv Robotics — research abstracts
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- arXiv Robotics — research abstractsSignalJEDI: Joint-Embedding Demonstration Compression for In-Context Imitation Learning
This research proposes JEDI, a method that compresses demonstrations into latent slots trained to predict future keyframes in a frozen video-JEPA space. Task and process counterfactuals help preserve execution details, and experiments on LIBERO and real robots show improved success and demonstration following over action-only compression.
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