Title: Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control Show Chinese title

Title: 基于模仿学习和力控制的可推广的人机协作装配

Abstract: Robots have been steadily increasing their presence in our daily lives, where they can work along with humans to provide assistance in various tasks on industry floors, in offices, and in homes. Automated assembly is one of the key applications of robots, and the next generation assembly systems could become much more efficient by creating collaborative human-robot systems. However, although collaborative robots have been around for decades, their application in truly collaborative systems has been limited. This is because a truly collaborative human-robot system needs to adjust its operation with respect to the uncertainty and imprecision in human actions, ensure safety during interaction, etc. In this paper, we present a system for human-robot collaborative assembly using learning from demonstration and pose estimation, so that the robot can adapt to the uncertainty caused by the operation of humans. Learning from demonstration is used to generate motion trajectories for the robot based on the pose estimate of different goal locations from a deep learning-based vision system. The proposed system is demonstrated using a physical 6 DoF manipulator in a collaborative human-robot assembly scenario. We show successful generalization of the system's operation to changes in the initial and final goal locations through various experiments. Show Chinese abstract

Abstract: 机器人在我们的日常生活中持续增加其存在，它们可以与人类一起工作，在工厂、办公室和家庭的各种任务中提供帮助。 自动化装配是机器人的一项关键应用，下一代装配系统可以通过创建协作的人机系统变得更加高效。 然而，尽管协作机器人已经存在了几十年，但它们在真正协作系统中的应用仍然有限。 这是因为一个真正的协作人机系统需要根据人类动作的不确定性和不精确性调整其操作，确保交互过程中的安全性等。 在本文中，我们提出了一种基于示范学习和姿态估计的人机协作装配系统，使机器人能够适应由人类操作引起的不确定性。 示范学习用于根据基于深度学习的视觉系统对不同目标位置的姿态估计，为机器人生成运动轨迹。 所提出的系统在一个协作人机装配场景中使用了一个物理的6自由度机械臂进行演示。 我们通过各种实验展示了系统操作对初始和最终目标位置变化的成功泛化。