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New Time-to-Goal metric boosts AI planning with JEPA world models

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Time-to-Goal (T2G), a novel metric designed to enhance long-horizon planning in AI systems that utilize Joint-Embedding Predictive Architectures (JEPAs). Standard JEPAs can struggle with temporal ordering, leading to similar costs for states at different time distances from a goal. T2G addresses this by learning an explicit temporal ordering from offline trajectories, encouraging lower costs for states closer to the goal without requiring reward signals or policy labels. When integrated into a flat LeWorldModel, T2G significantly improved planning success rates across navigation and manipulation tasks, outperforming existing hierarchical JEPA baselines. AI

IMPACT Enhances long-horizon planning capabilities in AI systems, potentially improving performance in complex robotic and navigation tasks.

RANK_REASON Academic paper detailing a new method for AI planning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Time-to-Goal metric boosts AI planning with JEPA world models

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Academic paper detailing a new method for AI planning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ruilong Yu, Fei Ye ·

    Time-to-Goal: Learned Temporal Metrics for Long-Horizon Planning with JEPA World Models

    arXiv:2611.05847v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) support planning by comparing predicted latent states with an encoded goal. For this comparison to guide temporal progress, states closer in time to the goal should receive lower co…

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