  Robotic manipulation in everyday scenarios, especially in unstructured
environments, requires skills in pose-aware object manipulation (POM), which
adapts robots' grasping and handling according to an object's 6D pose.
Recognizing an object's position and orientation is crucial for effective
manipulation. For example, if a mug is lying on its side, it's more effective
to grasp it by the rim rather than the handle. Despite its importance, research
in POM skills remains limited, because learning manipulation skills requires
pose-varying simulation environments and datasets. This paper introduces
ManiPose, a pioneering benchmark designed to advance the study of pose-varying
manipulation tasks. ManiPose encompasses: 1) Simulation environments for POM
feature tasks ranging from 6D pose-specific pick-and-place of single objects to
cluttered scenes, further including interactions with articulated objects. 2) A
comprehensive dataset featuring geometrically consistent and
manipulation-oriented 6D pose labels for 2936 real-world scanned rigid objects
and 100 articulated objects across 59 categories. 3) A baseline for POM,
leveraging the inferencing abilities of LLM (e.g., ChatGPT) to analyze the
relationship between 6D pose and task-specific requirements, offers enhanced
pose-aware grasp prediction and motion planning capabilities. Our benchmark
demonstrates notable advancements in pose estimation, pose-aware manipulation,
and real-robot skill transfer, setting new standards for POM research. We will
open-source the ManiPose benchmark with the final version paper, inviting the
community to engage with our resources, available at our
website:https://sites.google.com/view/manipose.
