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arXiv · 2610.40356

JEDI: Joint-Embedding Demonstration Compression for In-Context Imitation Learning

Jiazhao Liang·Zhewen He·Shuaihang Yuan·Hao Huang·Yi Fang
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Abstract

In-context imitation learning (ICIL) adapts robot policies through test-time demonstrations, but long multimodal trajectories constrain how many examples fit in context. We ask what a compressed demonstration should preserve. We propose JEDI, which compresses demonstrations into latent slots trained to predict the query episode's next-stage keyframe in a frozen video-JEPA space. Task and process counterfactuals help the slots preserve execution details, while the predictor selects demonstrations from a bank. Post-training with keypoint tokens enables human-hand conditioning on robots. Experiments on LIBERO and real robots show that JEDI supports many-shot context; LIBERO objective controls show improved success and demonstration following over action-only compression. These findings support query-future prediction as a training objective for preserving execution-relevant information in compressed demonstrations.

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Jiazhao Liang, Zhewen He, Shuaihang Yuan, Hao Huang, Yi Fang. 2026-10-29. JEDI: Joint-Embedding Demonstration Compression for In-Context Imitation Learning. https://arxiv.org/abs/2610.40356

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