<ul><li>Learning efficient representations for decision-making policies is a challenge in imitation learning (IL).</li><li>Self-supervised learning (SSL) offers an alternative by allowing models to learn from diverse, unlabeled data, including failures.</li><li>ACT-JEPA is a novel architecture that integrates IL and SSL to enhance policy representations.</li><li>ACT-JEPA improves the quality of representations by learning temporal environment dynamics and effectively generalizes to action sequence prediction.</li></ul>

ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning

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