  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/.
