RoboCasa：面向通用机器人的日常任务大规模仿真
RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
AI总结：
本文提出大规模仿真框架RoboCasa，通过丰富厨房场景3D资产与生成式AI工具，结合人类演示与自动化轨迹生成扩充数据集，支持100项任务评估，验证了合成数据在模仿学习中的规模化趋势及向真实世界迁移的潜力。
摘要：
人工智能（AI）领域的最新进展很大程度上得益于规模化的推动。在机器人领域，由于缺乏大规模机器人数据集，规模化进程受到了阻碍。我们主张利用逼真的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。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仿真框架。RoboCasa 具有逼真且多样化的场景，重点关注厨房环境。我们提供了跨越 150 多个物体类别的数千个 3D 资产，以及数十种可交互的家具和电器。我们利用生成式 AI 工具丰富了仿真的逼真度和多样性，例如从文本到 3D 模型获取物体资产，以及从文本到图像模型获取环境纹理。我们设计了一组 100 个任务用于系统评估，包括由大型语言模型引导生成的复合任务。为了促进学习，我们提供了高质量的人类演示，并集成了自动化轨迹生成方法，以最小化的人类负担大幅扩充了我们的数据集。我们的实验表明，在使用合成生成的机器人数据进行大规模模仿学习方面存在明显的规模化趋势，并展示了利用仿真数据在真实世界任务中的巨大前景。视频和开源代码可在 https://robocasa.ai/ 获取。
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
