kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation

TL;DR

kPAM introduces a category-level robotic manipulation approach using semantic 3D keypoints, enabling flexible, interpretable, and robust object placement despite large shape variations within object categories.

Contribution

It proposes a novel keypoint-based object representation for category-level manipulation, replacing traditional pose estimation, and integrates it into a perception-to-action pipeline.

Findings

Successfully manipulates unseen objects within categories.

Robustly handles large intra-category shape variations.

Achieves reliable placement of shoes and mugs in hardware experiments.

Abstract

We would like robots to achieve purposeful manipulation by placing any instance from a category of objects into a desired set of goal states. Existing manipulation pipelines typically specify the desired configuration as a target 6-DOF pose and rely on explicitly estimating the pose of the manipulated objects. However, representing an object with a parameterized transformation defined on a fixed template cannot capture large intra-category shape variation, and specifying a target pose at a category level can be physically infeasible or fail to accomplish the task -- e.g. knowing the pose and size of a coffee mug relative to some canonical mug is not sufficient to successfully hang it on a rack by its handle. Hence we propose a novel formulation of category-level manipulation that uses semantic 3D keypoints as the object representation. This keypoint representation enables a simple and interpretable specification of the manipulation target as geometric costs and constraints on the keypoints, which flexibly generalizes existing pose-based manipulation methods. Using this formulation, we factor the manipulation policy into instance segmentation, 3D keypoint detection, optimization-based robot action planning and local dense-geometry-based action execution. This factorization allows us to leverage advances in these sub-problems and combine them into a general and effective perception-to-action manipulation pipeline. Our pipeline is robust to large intra-category shape variation and topology changes as the keypoint representation ignores task-irrelevant geometric details. Extensive hardware experiments demonstrate our method can reliably accomplish tasks with never-before seen objects in a category, such as placing shoes and mugs with significant shape variation into category level target configurations.