RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

By Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, Yuke Zhu. arXiv.org, 2024

419 citations recorded by the source corpus.

Recent advancements in Artificial Intelligence (AI) have largely been propelled by scaling. In Robotics, scaling is hindered by the lack of access to massive robot datasets. We advocate using realistic physical simulation as a means to scale environments, tasks, and datasets for robot learning methods. We present RoboCasa, a large-scale simulation framework for training generalist robots in everyday environments. RoboCasa features realistic and diverse scenes focusing on kitchen environments. We provide thousands of 3D assets across over 150 object categories and dozens of interactable furniture and appliances. We enrich the realism and diversity of our simulation with generative AI tools, such as object assets from text-to-3D models and environment textures from text-to-image models. We design a set of 100 tasks for systematic evaluation, including composite tasks generated by the guidance of large language models. To facilitate learning, we provide high-quality human demonstrations and integrate automated trajectory generation methods to substantially enlarge our datasets with minimal human burden. Our experiments show a clear scaling trend in using synthetically generated robot data for large-scale imitation learning and show great promise in harnessing simulation data in real-world tasks. Videos and open-source code are available at https://robocasa.ai/

Subfields: Artificial Intelligence, Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/f773ceb43ffc7f9cd685250c4919924ec0bc85c7

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Citations in the corpus

GR00T N1: An Open Foundation Model for Generalist Humanoid Robots (2025, positive): “We observe that GR00T N1 co-trained with neural trajectories consistently results in substantial gains compared to GR00T N1 only trained on real-world trajectories: +4.2%, +8.8%, +6.8% on average for 30, 100, and 300 data-regimes, respectively, for RoboCasa and +5.8% on average across the 8 tasks wi”
RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation (2025, positive): “For each task, we collect 50 expert demonstrations in a clean simulation environment without domain randomization, which are used for training.”