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arXiv Computation and Language By Jianshu Zhang, Keliang Wu, Chengxuan Qian, Xiyuan Yang, Ce Zhang, Ariel Tian, Anbang Liu, Haoran Lu, Han Liu

ProgressCompass: Embodied Progress Reward Models Are Lost Without the Right Context

Read the original on arXiv Computation and Language →

The paper introduces ProgressCompass, a framework that enhances Embodied Progress Reward Models (PRMs) by providing the necessary contextual information for accurate progress estimation in long manipulation tasks. It presents ContextProgress-Bench, a benchmark with 24 tasks that tests PRMs under three context-dependent scenarios—State Recall, Sequence Tracking, and Recurrence Disambiguation—showing that even history-aware PRMs struggle without proper context. By integrating a context-aware loop that leverages general-purpose vision‑language models, ProgressCompass reduces PRM progress error by up to 82% and improves rank agreement by 76%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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