PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment

Hugging Face Daily Papers Papers

Summary

PRM-as-a-Judge 1.5 is a toolkit that provides fine-grained metrics and reliability tools for evaluating embodied robotic models, moving beyond binary success rates to assess process progress and execution quality.

Fine-grained robotic evaluation matters for understanding embodied models, going beyond binary success rates and rule-based process scores. We present PRM-as-a-Judge 1.5, a toolkit for robot process assessment that turns rollout videos into dense progress curves and derives multiple fine metrics. PRM-as-a-Judge 1.5 introduces three metrics, building on version 1.0, that characterize failure-side progress, post-drawdown recovery, and success-side execution quality, helping users understand embodied model capability. Based on the rollout videos from benchmarks, we perform a comprehensive assessment of the embodied models, providing some fine-grained metric results and key findings. We further introduce RoboPulse++ to evaluate the reliability of process reward models (PRM), providing evaluators with a more accurate testing platform. Moreover, we release a user-friendly assessment suite, including the benchmark, metric implementation, and visualization tools, to support reproducible manipulation process evaluation. We call on the community to rethink how robots are evaluated and establish transparent, procedural, and reproducible assessment as a foundation for the next generation of embodied intelligence.
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Abstract

PRM-as-a-Judge 1.5 provides fine-grained process metrics and reliability tools to evaluate embodied robotic models beyond binary success rates.

Fine-grained robotic evaluation matters for understandingembodied models, going beyond binary success rates and rule-based process scores. We present PRM-as-a-Judge 1.5, a toolkit for robot process assessment that turnsrollout videosinto denseprogress curvesand derives multiple fine metrics. PRM-as-a-Judge 1.5 introduces three metrics, building on version 1.0, that characterizefailure-side progress,post-drawdown recovery, and success-sideexecution quality, helping users understand embodied model capability. Based on therollout videosfrom benchmarks, we perform a comprehensive assessment of theembodied models, providing some fine-grained metric results and key findings. We further introduceRoboPulse++to evaluate the reliability ofprocess reward models(PRM), providing evaluators with a more accurate testing platform. Moreover, we release a user-friendly assessment suite, including the benchmark, metric implementation, and visualization tools, to support reproducible manipulation process evaluation. We call on the community to rethink how robots are evaluated and establish transparent, procedural, and reproducible assessment as a foundation for the next generation of embodied intelligence.

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