JEPA Policy: Diffusion-Free Imitation Learning via Paired Action and Future Representation Prediction

2026-09-08

AI Summary

JEPA Policy通过配对动作和未来表示预测，使用共享Transformer框架改进模仿学习，无需扩散过程，提高任务成功率并降低延迟。

Abstract

Standard behavior cloning supervises actions without explicitly constraining the future representation paired with each demonstrated action chunk. We introduce JEPA Policy, a diffusion-free framework that uses the action chunk and its observed future representation as paired training targets. Action and future-representation tokens interact in a shared Transformer and are refined through two forward passes. Future prediction can therefore shape the representation used to generate actions. Dual-branch and gradient-routing controls attribute the gain to this shared topology rather than to an auxiliary prediction head alone. Across nine simulated tasks, JEPA Policy improves mean success over the action-only MIP baseline and outperforms Diffusion Policy under the evaluated configurations, while adding 0.29 ms to MIP's model latency. A five-task, 630-episode physical-robot study produces the same pooled ranking. Further audits find no complete representation collapse under action supervision and identify a task-conditioned failure-ranking signal in future-prediction error. These results support paired future-representation supervision as a practical approach to low-latency visuomotor imitation without iterative generative sampling.

Problem

• behavior cloning
• future representation
• imitation learning

Innovation

• JEPA Policy
• shared Transformer
• future-representation prediction
• low-latency imitation learning
• paired training targets