The choice of a grasp plays a critical role in the success of downstream manipulation 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 an object in a cluttered scene to develop an algorithm for task-aware grasp estimation. We present an object-centric action space that encodes the relationship between the geometry of the placement scene and the object to be placed in order to provide placement affordance maps directly from perspective views of the placement scene. This action space enables the computation of a one-to-one mapping between the placement and picking actions allowing the robot to generate a diverse set of pick-and-place proposals and to optimize for a grasp under other task constraints such as robot kinematics and collision avoidance. With experiments both in simulation and on a real robot we demonstrate that with our method, the robot is able to successfully complete the task of placement-aware grasping with over 89% accuracy in such a way that generalizes to novel objects and scenes.

翻译：抓取选择对下游操作任务的成败至关重要。考虑在杂乱场景中放置物体的任务：大多数可能的抓取方式可能并不适合预期的放置需求。本文研究了杂乱场景中抓取与放置的协同作用，提出了一种任务感知抓取估计算法。我们提出了一种以物体为中心的动作空间，该空间通过编码放置场景几何结构与待放置物体之间的关系，直接从放置场景的视角图中生成放置可供性地图。该动作空间支持计算放置与抓取动作之间的一一映射，使得机器人能够生成多样化的抓放方案，并在机器人运动学和碰撞规避等其他任务约束下优化抓取。通过在仿真环境和真实机器人上的实验证明，采用我们的方法，机器人能够以超过89%的准确率成功完成放置感知抓取任务，并且该方法能够泛化至未见过的物体和场景。