PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment
- URL: http://arxiv.org/abs/2608.14284v1
- Date: Fri, 14 Aug 2026 13:10:02 GMT
- Title: PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment
- Abstract summary: We present PRM-as-a-Judge 1.5, a toolkit for robot process assessment.<n>It turns rollout videos into dense progress curves and derives multiple fine metrics.<n>We also introduce RoboPulse++ to evaluate the reliability of process reward models.
- Score: 34.01922956216341
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- R2S-Eval: Robot Evaluation with Real-to-Sim Calibration via Vision-Language Models [12.45799943639499]
R2S-Eval is an evaluation pipeline that combines real-to-sim calibration with vision-language model (VLM) preference evaluation.<n>It produces reliable and stable policy conclusions, achieves agreement with human preferences, substantially reduces repeated hardware-operation effort, and reveals behavior-quality differences that are not captured by binary success labels.
arXiv Detail & Related papers (2026-09-03T02:08:16Z) - Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models [74.1961755473754]
Existing evaluation methods rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations.<n>We propose the Evaluation Agent framework, which employs human-like strategies for efficient, dynamic, multi-round evaluations.<n>We show that Evaluation Agent reduces evaluation time to 10% of traditional methods while delivering comparable results.
arXiv Detail & Related papers (2026-08-10T14:42:10Z) - How can we assess human-agent interactions? Case studies in software agent design [52.953425368394306]
We make two major steps towards the rigorous assessment of human-agent interactions.<n>We propose PULSE, a framework for more efficient human-centric evaluation of agent designs.<n>We deploy the framework on a large-scale web platform built around the open-source software agent OpenHands.
arXiv Detail & Related papers (2025-10-10T19:04:28Z) - SCAN: Structured Capability Assessment and Navigation for LLMs [54.54085382131134]
textbfSCAN (Structured Capability Assessment and Navigation) is a practical framework that enables detailed characterization of Large Language Models.<n>SCAN incorporates four key components:.<n>TaxBuilder, which extracts capability-indicating tags from queries to construct a hierarchical taxonomy;.<n>RealMix, a query synthesis and filtering mechanism that ensures sufficient evaluation data for each capability tag;.<n>A PC$2$-based (Pre-Comparison-derived Criteria) LLM-as-a-Judge approach achieves significantly higher accuracy compared to classic LLM-as-a-Judge method
arXiv Detail & Related papers (2025-05-10T16:52:40Z) - The Lessons of Developing Process Reward Models in Mathematical Reasoning [62.165534879284735]
Process Reward Models (PRMs) aim to identify and mitigate intermediate errors in the reasoning processes.<n>We develop a consensus filtering mechanism that effectively integrates Monte Carlo (MC) estimation with Large Language Models (LLMs)<n>We release a new state-of-the-art PRM that outperforms existing open-source alternatives.
arXiv Detail & Related papers (2025-01-13T13:10:16Z) - ToolComp: A Multi-Tool Reasoning & Process Supervision Benchmark [0.0]
We introduce ToolComp, a benchmark designed to evaluate multi-step tool-use reasoning.<n>ToolComp is developed through a collaboration between models and human annotators.<n>We generate synthetic training data to compare the performance of outcome-supervised reward models with process-supervised reward models.
arXiv Detail & Related papers (2025-01-02T15:10:52Z) - Automatic Evaluation for Text-to-image Generation: Task-decomposed Framework, Distilled Training, and Meta-evaluation Benchmark [62.58869921806019]
We propose a task decomposition evaluation framework based on GPT-4o to automatically construct a new training dataset.
We design innovative training strategies to effectively distill GPT-4o's evaluation capabilities into a 7B open-source MLLM, MiniCPM-V-2.6.
Experimental results demonstrate that our distilled open-source MLLM significantly outperforms the current state-of-the-art GPT-4o-base baseline.
arXiv Detail & Related papers (2024-11-23T08:06:06Z) - CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution [74.41064280094064]
textbfJudger-1 is the first open-source textbfall-in-one judge LLM.
CompassJudger-1 is a general-purpose LLM that demonstrates remarkable versatility.
textbfJudgerBench is a new benchmark that encompasses various subjective evaluation tasks.
arXiv Detail & Related papers (2024-10-21T17:56:51Z) - Self-Judge: Selective Instruction Following with Alignment Self-Evaluation [27.69410513313001]
We study the study of selective instruction following, whereby the system declines to execute instructions if the anticipated response quality is low.
We introduce Self-J, a novel self-training framework for developing judge models without needing human-annotated quality scores.
arXiv Detail & Related papers (2024-09-02T04:14:13Z) - QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement.
QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights.
We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
arXiv Detail & Related papers (2023-11-06T00:21:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.