RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies

[2604.09860] RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2604.09860 (cs) [Submitted on 10 Apr 2026 ( v1 ), last revised 14 Aug 2026 (this version, v4)] Title: RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies Authors: Jenai Xuning Yang , Rishit Dagli , Alex Zook , Hugo Hadfield , Ankit Goyal , Stan Birchfield , Fabio Ramos , Jonathan Tremblay View a PDF of the paper titled RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies, by Jenai Xuning Yang and 7 other authors View PDF HTML (experimental) Abstract: The pursuit of general-purpose robotics has yielded impressive foundation models, yet simulation-based benchmarking remains a bottleneck due to rapid performance saturation and a lack of true generalization testing. Existing benchmarks often exhibit significant domain overlap between training and evaluation, trivializing success rates and obscuring insights into robustness. We introduce RoboLab, a simulation benchmarking framework designed to address these challenges. Concretely, our framework is designed to answer two questions: (1) to what extent can we understand the performance of a real-world policy by analyzing its behavior in simulation, and (2) which factor most strongly affect policy behavior. First, RoboLab enables human-authored and LLM-enabled generation of scenes and tasks in a robot- and policy-agnostic manner within a high-fidelity simulation environment. We introduce an accompanying RoboLab-120 benchmark, consisting of 120 tasks categorized into three competency axes: visual, procedural, relational, across three difficulty levels. Second, we introduce a systematic analysis of real-world policies that quantify both their performance and the sensitivity of their behavior to controlled perturbations, exposing significant performance gap in current state-of-the-art models. By providing granular metrics and a scalable toolset, RoboLab offers a scalable framework for evaluating the true generalization capabilities of task-generalist robotic policies. Project website: this https URL . Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2604.09860 [cs.RO] (or arXiv:2604.09860v4 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2604.09860 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Robotics: Science and Systems XXII, Sydney, Australia, 2026 Submission history From: Jenai Xuning Yang [ view email ] [v1] Fri, 10 Apr 2026 19:42:21 UTC (14,889 KB) [v2] Tue, 14 Apr 2026 23:00:35 UTC (14,890 KB) [v3] Thu, 14 May 2026 17:31:57 UTC (18,461 KB) [v4] Fri, 14 Aug 2026 20:04:16 UTC (18,510 KB) Full-text links: Access Paper: View a PDF of the paper titled RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies, by Jenai Xuning Yang and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-04 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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