RoboCasa：通用机器人日常任务大规模模拟

摘要

人工智能（AI）领域的最新进展在很大程度上是通过扩展来推动的。在机器人领域，扩展受到无法获取大规模机器人数据集的阻碍。我们主张使用逼真的物理模拟作为一种手段，来扩展机器人学习方法的环境、任务和数据集。我们提出了RoboCasa，一个用于在日常环境中训练通用机器人的大规模模拟框架。RoboCasa具有逼真且多样化的场景，重点放在厨房环境上。我们提供了超过150个物体类别和数十个可互动家具和电器的数千个3D资产。我们利用生成式人工智能工具丰富了模拟的逼真性和多样性，例如从文本到3D模型的物体资产和从文本到图像的环境纹理。我们设计了一套包括通过大型语言模型指导生成的复合任务在内的100个任务，用于系统评估。为了促进学习，我们提供高质量的人类演示，并整合自动轨迹生成方法，以最小化人力负担大幅扩展我们的数据集。我们的实验显示，使用合成生成的机器人数据进行大规模模仿学习存在明显的扩展趋势，并展示了在实际任务中利用模拟数据的巨大潜力。视频和开源代码可在https://robocasa.ai/ 上获得。

English

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/

RoboCasa：通用机器人日常任务大规模模拟

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

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