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/

翻译：人工智能（AI）的最新进展主要由规模化所推动。在机器人学领域，规模化因缺乏大规模机器人数据集而受阻。我们主张使用高真实度物理仿真作为扩展机器人学习方法的环境、任务和数据集的手段。本文提出RoboCasa——一个用于在日常环境中训练通用机器人的大规模仿真框架。RoboCasa以厨房环境为核心，提供真实且多样化的场景。我们提供了涵盖150余个物体类别的数千个三维资源，以及数十种可交互的家具与电器。通过生成式AI工具（如基于文本到三维模型生成的物体资源，以及基于文本到图像模型生成的环境纹理），我们增强了仿真的真实性与多样性。我们设计了一套包含100项任务的系统性评估方案，其中包括由大语言模型指导生成的复合任务。为促进学习，我们提供高质量的人类示范数据，并集成自动轨迹生成方法，以最小的人力成本大幅扩展数据集。实验结果表明，使用合成生成的机器人数据进行大规模模仿学习具有明显的规模化趋势，并在利用仿真数据完成现实世界任务方面展现出巨大潜力。视频与开源代码详见 https://robocasa.ai/