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English(EN) Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control

Temporal-Distance JEPA 增强世界模型预测控制

研究人员推出了一种新颖的潜在世界模型预测控制表示学习方法——Temporal-Distance JEPA (TD-JEPA)。该方法通过从无奖励轨迹中挖掘定向时间成本来增强联合嵌入预测架构 (JEPAs),提高了根据目标进展对想象的未来进行排名的能力。TD-JEPA 在 Two-Room 和 OGB-Cube 等环境中展示了显著的性能提升,通过有效弥合训练和规划之间的差距,超越了先前的方法。 AI

影响 通过更好地理解所学习世界模型中的时间进展,提高了 AI 代理的规划能力。

排序理由 该集群包含一篇详细介绍 AI 中表示学习新方法的学术论文。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Temporal-Distance JEPA 增强世界模型预测控制

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Jiaxin Bai, Jiaxuan Xiong ·

    Temporal-Distance JEPA:用于潜在世界模型预测控制的感知规划表征学习

    arXiv:2607.25337v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting in representation space rather than reconstructing pixels, making them a natural backbone for latent model predictive control from offline demonstrati…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Temporal-Distance JEPA:用于潜在世界模型预测控制的感知规划表征学习

    Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting in representation space rather than reconstructing pixels, making them a natural backbone for latent model predictive control from offline demonstration logs. JEPA-style training optimizes short-hor…