[2603.04356] RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots

Computer Science > Robotics arXiv:2603.04356 (cs) [Submitted on 4 Mar 2026] Title:RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots Authors:Soroush Nasiriany, Sepehr Nasiriany, Abhiram Maddukuri, Yuke Zhu View a PDF of the paper titled RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots, by Soroush Nasiriany and Sepehr Nasiriany and Abhiram Maddukuri and Yuke Zhu View PDF HTML (experimental) Abstract:Recent advances in robot learning have accelerated progress toward generalist robots that can perform everyday tasks in human environments. Yet it remains difficult to gauge how close we are to this vision. The field lacks a reproducible, large-scale benchmark for systematic evaluation. To fill this gap, we present RoboCasa365, a comprehensive simulation benchmark for household mobile manipulation. Built on the RoboCasa platform, RoboCasa365 introduces 365 everyday tasks across 2,500 diverse kitchen environments, with over 600 hours of human demonstration data and over 1600 hours of synthetically generated demonstration data -- making it one of the most diverse and large-scale resources for studying generalist policies. RoboCasa365 is designed to support systematic evaluations for different problem settings, including multi-task learning, robot foundation model training, and lifelong learning. We conduct extensive experiments on this benchmark with state-of-the-art methods and analyze the...