We present Learning to Place by Picking (LPP), a method capable of autonomously collecting demonstrations for a family of placing tasks in which objects must be manipulated to specific locations. With LPP, we approach the learning of robotic object placement policies by reversing the grasping process and exploiting the inherent symmetry of the pick and place problems. Specifically, we obtain placing demonstrations from a set of grasp sequences of objects that are initially located at their target placement locations. Our system is capable of collecting hundreds of demonstrations without human intervention by using a combination of tactile sensing and compliant control for grasps. We train a policy directly from visual observations through behaviour cloning, using the autonomously-collected demonstrations. By doing so, the policy can generalize to object placement scenarios outside of the training environment without privileged information (e.g., placing a plate picked up from a table and not at the original placement location). We validate our approach on home robotic scenarios that include dishwasher loading and table setting. Our approach yields robotic placing policies that outperform policies trained with kinesthetic teaching, both in terms of performance and data efficiency, while requiring no human supervision.

通过反转抓取过程并利用抓取和放置问题的对称性，我们提出了自动收集放置任务学习示范的方法Learning to Place by Picking (LPP)。通过触觉感知和顺应性控制结合，我们的系统能够在无人干预的情况下收集数百个示范，并通过行为克隆直接从视觉观察中训练策略。在家庭机器人场景中进行验证，包括装载洗碗机和摆台餐具，我们的方法得到了优于基于肌动学教学的策略的结果，同时无需人类监督。

倒推学习: 通过挑选学习摆放