Time-to-Goal: Learned Temporal Metrics for Long-Horizon Planning with JEPA World Models
Overview
This research paper presents Time-to-Goal (T2G), a method that learns temporal ordering metrics for goal costs in Joint-Embedding Predictive Architectures (JEPAs) to improve long-horizon planning. By training on latent triples from offline trajectories and using a margin ranking loss, T2G enhances temporal contrast without requiring rewards or policy labels. Evaluated across four navigation and manipulation benchmarks, the approach raises average planning success rates from 32.7% to 63.0%, outperforming flat LeWorldModel baselines and approaching hierarchical H-JEPA performance.
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- arXiv Robotics — research abstractsSignalTime-to-Goal: Learned Temporal Metrics for Long-Horizon Planning with JEPA World Models
This research paper presents Time-to-Goal (T2G), a method that learns temporal ordering metrics for goal costs in Joint-Embedding Predictive Architectures (JEPAs) to improve long-horizon planning. By training on latent triples from offline trajectories and using a margin ranking loss, T2G enhances temporal contrast without requiring rewards or policy labels. Evaluated across four navigation and manipulation benchmarks, the approach raises average planning success rates from 32.7% to 63.0%, outperforming flat LeWorldModel baselines and approaching hierarchical H-JEPA performance.
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Current attention 3·Peak within the comparable range 9（2026-11-05 07:00 UTC）·Change within the comparable range over 24 hours -49%
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