论文标题
自我监督的目标条件和地点
Self-Supervised Goal-Conditioned Pick and Place
论文作者
论文摘要
机器人有能力通过与世界上的对象进行交互来自主收集大量数据。但是，在没有人类标记的监督的情况下，从自主收集的数据中学习的方式通常并不明显。在这项工作中，我们从无监督的选择中学习像素对象表示，并将其推广到新对象的数据。我们引入了一个新颖的框架，用于使用这些表示形式，以预测在哪里选择以及在哪里放置以匹配目标图像。最后，我们在模拟的抓握环境中演示了我们方法的实用性。
Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world. However, it is often not obvious \emph{how} to learning from autonomously collected data without human-labeled supervision. In this work we learn pixel-wise object representations from unsupervised pick and place data that generalize to new objects. We introduce a novel framework for using these representations in order to predict where to pick and where to place in order to match a goal image. Finally, we demonstrate the utility of our approach in a simulated grasping environment.
