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The choice of a grasp plays a critical role in the success of downstreammanipulation tasks. Consider a task of placing an object in a cluttered scene;the majority of possible grasps may not be suitable for the desired placement.In this paper, we study the synergy between the picking and placing of anobject in a cluttered scene to develop an algorithm for task-aware graspestimation. We present an object-centric action space that encodes therelationship between the geometry of the placement scene and the object to beplaced in order to provide placement affordance maps directly from perspectiveviews of the placement scene. This action space enables the computation of aone-to-one mapping between the placement and picking actions allowing the robotto generate a diverse set of pick-and-place proposals and to optimize for agrasp under other task constraints such as robot kinematics and collisionavoidance. With experiments both in simulation and on a real robot wedemonstrate that with our method, the robot is able to successfully completethe task of placement-aware grasping with over 89% accuracy in such a way thatgeneralizes to novel objects and scenes.

Pick2Place: Task-aware 6DoF Grasp Estimation via Object-Centric Perspective Affordance