RoboCasa365：一种大规模仿真框架，用于训练和评估通用机器人
RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots
AI总结：
RoboCasa365通过大规模仿真框架提供多样化任务和环境，用于训练和评估通用机器人，分析任务多样性、数据集规模和环境变化对泛化的影响。
摘要：
近期机器人学习的进步加速了向能够执行人类环境日常任务的通用机器人迈进的步伐。然而，仍难以衡量我们距离这一愿景有多近。该领域缺乏可重复、大规模的基准用于系统性评估。为填补这一空白，我们提出了RoboCasa365，一种全面的家居移动操作仿真基准。基于RoboCasa平台，RoboCasa365引入了365个日常任务，覆盖2500种多样的厨房环境，包含超过600小时的人类演示数据和超过1600小时的合成演示数据——使其成为研究通用策略最多样和大规模的资源之一。RoboCasa365旨在支持不同问题设置的系统性评估，包括多任务学习、机器人基础模型训练和终身学习。我们对这个基准进行了广泛的实验，使用最先进的方法，并分析了任务多样性、数据集规模和环境变化对泛化能力的影响。我们的结果提供了新的见解，揭示了哪些因素最强烈地影响通用机器人的性能，并为未来该领域的发展提供了策略指导。
英文摘要：
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 impacts of task diversity, dataset scale, and environment variation on generalization. Our results provide new insights into what factors most strongly affect the performance of generalist robots and inform strategies for future progress in the field.
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。
