NVIDIA Research, in collaboration with the University of Sydney and the University of Toronto, has introduced RoboLab, a new simulation benchmarking platform designed to address critical gaps in evaluating general-purpose robot policies. Published in July 2026 by the Seattle Robotics Lab, the initiative responds to the rapid advancement of robotics foundation models capable of following natural language instructions for complex manipulation tasks. As these systems grow more capable, the field faces a pressing need for rigorous, reproducible evaluation standards that current benchmarks fail to provide. Existing evaluation frameworks suffer from several fundamental limitations. Training and testing data frequently share identical visual sources, allowing models to memorize environments rather than demonstrate true generalization. Additionally, static task sets quickly saturate performance metrics, rendering incremental improvements invisible. The field also lacks diagnostic depth, relying on binary success or failure scores that obscure the root causes of errors. Finally, most published benchmarks operate with insufficient rollout counts, compromising the statistical reliability of reported success rates. RoboLab addresses these shortcomings through a platform built on three core principles: robot-agnostic task evaluation, rapid task generation to prevent benchmark saturation, and comprehensive diagnostic analytics. The system enables researchers to deploy any policy or robot embodiment against a standardized suite of scenarios. To combat saturation, RoboLab incorporates agentic AI workflows that autonomously generate novel tasks, ensuring the benchmark evolves alongside model capabilities. The initial RoboLab-120 benchmark curates 120 tabletop pick-and-place tasks explicitly tagged against three distinct competencies: visual recognition, procedural manipulation, and relational spatial logic. Beyond standard success rates, the platform emphasizes granular performance analysis. An integrated event logging system automatically records specific failure modes, such as incorrect object grasps, collisions, or dropped items, transforming post-hoc debugging into a real-time diagnostic process. The platform also evaluates policy robustness across three complexity dimensions. Language complexity tests measure resilience against vague or highly detailed phrasing. Scene complexity assesses performance amid visual clutter and distractors. Task horizon analysis tracks accuracy degradation over multi-step sequences, revealing how early failures cascade into complete task abandonment. To isolate the environmental factors driving performance drops, RoboLab employs sensitivity analysis powered by Neural Posterior Estimation. This approach quantifies the impact of simultaneous variations in camera placement, object distribution, and lighting without requiring exponentially large test suites. Statistical rigor is enforced through Clopper-Pearson confidence intervals, ensuring that comparative policy evaluations meet rigorous significance thresholds. RoboLab's architecture directly supports NVIDIA Isaac Lab-Arena, an open-source simulation framework for large-scale policy deployment. By shifting the industry focus from static scoring to dynamic, diagnostic evaluation, the platform establishes a scalable pathway for validating robotics models before real-world commercialization. NVIDIA has announced plans to integrate key RoboLab features into commercial products by August 2026, signaling a structured approach to aligning simulation standards with practical robotic deployment requirements.