  In this paper, we explore generalizable, perception-to-action robotic
manipulation for precise, contact-rich tasks. In particular, we contribute a
framework for closed-loop robotic manipulation that automatically handles a
category of objects, despite potentially unseen object instances and
significant intra-category variations in shape, size and appearance. Previous
approaches typically build a feedback loop on top of a real-time 6-DOF pose
estimator. However, representing an object with a parameterized transformation
from a fixed geometric template does not capture large intra-category shape
variation. Hence we adopt the keypoint-based object representation proposed in
kPAM for category-level pick-and-place, and extend it to closed-loop
manipulation policies with contact-rich tasks. We first augment keypoints with
local orientation information. Using the oriented keypoints, we propose a novel
object-centric action representation in terms of regulating the linear/angular
velocity or force/torque of these oriented keypoints. This formulation is
surprisingly versatile -- we demonstrate that it can accomplish contact-rich
manipulation tasks that require precision and dexterity for a category of
objects with different shapes, sizes and appearances, such as peg-hole
insertion for pegs and holes with significant shape variation and tight
clearance. With the proposed object and action representation, our framework is
also agnostic to the robot grasp pose and initial object configuration, making
it flexible for integration and deployment.
