赋能具身操作：面向家务任务的双臂移动机器人操作数据集
Empowering Embodied Manipulation: A Bimanual-Mobile Robot Manipulation Dataset for Household Tasks
AI总结：
针对双臂移动机器人家务操作数据匮乏问题，提出BRMData数据集，涵盖10项多样化家务任务并引入操作效率评分MES指标，以推动双臂移动操作技术发展。
摘要：
具身人工智能的进步正日益使机器人能够应对复杂的现实世界任务，例如家务操作。然而，机器人在这些环境中的部署仍然受到缺乏可学习的全面双臂移动机器人操作数据的制约。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能力、任务多样性、全面的传感器数据和稳健的评估指标；它们未能捕捉双臂移动机器人预期执行的家务操作任务所具有的复杂和动态特性。为了克服这些局限，我们提出了BRMData，一个专为家务应用设计的双臂移动机器人操作数据集。BRMData涵盖10项多样化的家务任务，包括单臂和双臂任务，以及桌面操作和移动操作，并利用多视角和深度传感数据信息。此外，BRMData具有难度递增的任务特点，从单物体到多物体抓取、从非交互到人机交互场景、从刚性物体到柔性物体操作，紧密模拟真实世界的家务应用。另外，我们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务任务中的精度和效率。我们使用BRMData对先进的机器人操作学习方法的性能进行了全面评估和分析，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，可在https://embodiedrobot.github.io/获取。
英文摘要：
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/.
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。
