论文标题
基于视觉的机器人操纵通过人类示范学习
Vision-based Robot Manipulation Learning via Human Demonstrations
论文作者
论文摘要
基于视觉的学习方法为机器人提供了学习复杂的操纵任务的希望。但是，如何将学习的操纵技巧推广到现实世界的互动仍然是一个悬而未决的问题。在这项工作中，我们通过在计算机视觉中使用活动识别和对象检测来研究从单人称观点演示中学习机器人操纵技能。为了促进对象和环境之间的概括，我们建议以文本语料库的形式使用先验的知识库来推断在机器人的上下文中要与之交互的对象。我们使用在日常生活中通常执行的几个简单而复杂的操纵任务来评估现实世界机器人的方法。实验结果表明，即使少量培训数据，我们的方法也能达到良好的概括性能。
Vision-based learning methods provide promise for robots to learn complex manipulation tasks. However, how to generalize the learned manipulation skills to real-world interactions remains an open question. In this work, we study robotic manipulation skill learning from a single third-person view demonstration by using activity recognition and object detection in computer vision. To facilitate generalization across objects and environments, we propose to use a prior knowledge base in the form of a text corpus to infer the object to be interacted with in the context of a robot. We evaluate our approach in a real-world robot, using several simple and complex manipulation tasks commonly performed in daily life. The experimental results show that our approach achieves good generalization performance even from small amounts of training data.
