Article

NimCritic: An Action-Aware Quantification Model for Long-Horizon Manipulation Progress Prediction

The study proposes an action-aware quantification method that uses a vision-language model to decompose long-horizon manipulation trajectories into object-level subtasks and assign weighted action credits, and builds a training video dataset of more than 800 hours plus the LOOM benchmark, which contains 19,618 human-calibrated evaluation frames.

Part of the event: NimCritic Introduces Action-Aware Quantification and Long-Horizon Progress Benchmark

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