The advancements in embodied AI are increasingly enabling robots to tackle complex real-world tasks, such as household manipulation. However, the deployment of robots in these environments remains constrained by the lack of comprehensive bimanual-mobile robot manipulation data that can be learned. Existing datasets predominantly focus on single-arm manipulation tasks, while the few dual-arm datasets available often lack mobility features, task diversity, comprehensive sensor data, and robust evaluation metrics; they fail to capture the intricate and dynamic nature of household manipulation tasks that bimanual-mobile robots are expected to perform. To overcome these limitations, we propose BRMData, a Bimanual-mobile Robot Manipulation Dataset specifically designed for household applications. BRMData encompasses 10 diverse household tasks, including single-arm and dual-arm tasks, as well as both tabletop and mobile manipulations, utilizing multi-view and depth-sensing data information. Moreover, BRMData features tasks of increasing difficulty, ranging from single-object to multi-object grasping, non-interactive to human-robot interactive scenarios, and rigid-object to flexible-object manipulation, closely simulating real-world household applications. Additionally, we introduce a novel Manipulation Efficiency Score (MES) metric to evaluate both the precision and efficiency of robot manipulation methods in household tasks. We thoroughly evaluate and analyze the performance of advanced robot manipulation learning methods using our BRMData, aiming to drive the development of bimanual-mobile robot manipulation technologies. The dataset is now open-sourced and available at https://embodiedrobot.github.io/.

翻译：具身人工智能的进步正日益使机器人能够处理复杂的现实世界任务，例如家庭环境中的操作。然而，由于缺乏可供学习的全面双臂移动机器人操作数据，机器人在此类环境中的部署仍然受到限制。现有数据集主要集中于单臂操作任务，而少数可用的双臂数据集往往缺乏移动性特征、任务多样性、全面的传感器数据以及稳健的评估指标；它们未能捕捉到双臂移动机器人预期执行的家庭操作任务所具有的复杂性和动态性。为克服这些局限，我们提出了BRMData，一个专门为家庭应用设计的双臂移动机器人操作数据集。BRMData涵盖了10种不同的家庭任务，包括单臂和双臂任务，以及桌面和移动操作，并利用了多视角和深度传感数据信息。此外，BRMData设计了难度递增的任务，范围从单物体抓取到多物体抓取、非交互到人机交互场景，以及刚体到柔性物体操作，紧密模拟了现实世界的家庭应用。我们还引入了一种新颖的操作效率评分（MES）指标，用于评估机器人操作方法在家庭任务中的精度和效率。我们利用BRMData全面评估和分析了先进机器人操作学习方法的性能，旨在推动双臂移动机器人操作技术的发展。该数据集现已开源，可通过 https://embodiedrobot.github.io/ 获取。