逆向工作：通过抓取学习放置
Working Backwards: Learning to Place by Picking
AI总结：
本文提出PvP方法，通过反转抓取过程自主收集接触受限放置任务的演示数据，并利用行为克隆训练视觉策略，在家庭机器人场景中实现优于动觉示教且无需人工监督的放置性能。
摘要：
我们提出通过抓取实现放置（placing via picking，PvP），这是一种为一类放置任务自主收集真实世界演示的方法，在这类任务中，物体必须被操控到特定的、接触受限的位置。借助 PvP，我们通过反转抓取过程并利用抓取与放置问题固有的对称性来处理机器人物体放置演示的收集。具体来说，我们从初始位于目标放置位置的物体的抓取序列集合中获取放置演示。我们的系统使用两个模块——用于抓取的柔顺控制和触觉重新抓取——能够在接触受限环境中无需人工干预地收集数百个演示。我们利用自主收集的演示，通过行为克隆直接从视觉观测训练策略。通过这种方式，该策略能够泛化到训练环境之外的物体放置场景，而无需特权信息（例如，放置从桌子上拿起的盘子）。我们在包括洗碗机装载和餐桌布置在内的家庭机器人场景中验证了我们的方法。我们的方法所产生的机器人放置策略在成功率和数据效率方面均优于通过动觉示教训练的策略，同时无需任何人工监督。
英文摘要：
We present placing via picking (PvP), a method to autonomously collect real-world demonstrations for a family of placing tasks in which objects must be manipulated to specific, contact-constrained locations. With PvP, we approach the collection of robotic object placement demonstrations 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 initially located at their target placement locations. Our system can collect hundreds of demonstrations in contact-constrained environments without human intervention using two modules: compliant control for grasping and tactile regrasping. We train a policy directly from visual observations through behavioural 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). We validate our approach in home robot 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 success rate and data efficiency, while requiring no human supervision.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
