Motion retargeting from human to robot remains a very challenging task due to variations in the structure of humans and robots. Most traditional optimization-based algorithms solve this problem by minimizing an objective function, which is usually time-consuming and heavily dependent on good initialization. In contrast, methods with feedforward neural networks can learn prior knowledge from training data and quickly infer the results, but these methods also suffer from the generalization problem on unseen actions, leading to some infeasible results. In this paper, we propose a novel neural optimization approach taking advantages of both kinds of methods. A graph-based neural network is utilized to establish a mapping between the latent space and the robot motion space. Afterward, the retargeting results can be obtained by searching for the optimal vector in this latent space. In addition, a deep encoder also provides a promising initialization for better and faster convergence. We perform experiments on retargeting Chinese sign language to three different kinds of robots in the simulation environment, including ABB's YuMi dual-arm collaborative robot, NAO and Pepper. A real-world experiment is also conducted on the Yumi robot. Experimental results show that our method can retarget motion from human to robot with both efficiency and accuracy.
翻译:由于人类和机器人结构的变异,重新定位人类到机器人仍然是一项极具挑战性的任务。由于人类和机器人结构的变异,大多数传统的优化型算法都通过尽量减少一个客观功能来解决这个问题,该功能通常耗时且严重依赖良好的初始化。相反,进食前神经网络的方法可以从培训数据中学习先学知识,并快速推导结果,但这些方法也因在不可见行动上的普遍化问题而受到影响,从而导致一些不可行的结果。在本文件中,我们提议采用新的神经优化方法,利用两种方法的优势。基于图形的神经网络用来在潜伏空间和机器人运动空间之间建立绘图。随后,通过在这个潜伏空间中寻找最佳矢量可以取得重新定位的结果。此外,深层的电解码器还可以提供一种有希望的初始化,以便更好和更快的趋同。我们在模拟环境中将中文符号语言重新定位为三种不同的机器人,包括ABB的Yumi双臂协作机器人、NAO和Pepper。在Yummi机器人的精确度上进行真实世界实验,并且从Yum机器人的实验结果显示我们的机器人的精确性。