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This paper aims to develop an extended teaching by demonstration (TbD) system which can also enable learning stiffness regulation strategies from humans. TbD is a promising way for robot learning skills in human and robot collaborative hybrid manufacturing lines. Traditionally, TbD systems have only concentrated on how to enable robots to learn movement skills from humans. The authors propose an extended dynamical motor primitives (DMP) framework to achieve this goal. In addition to the advantages of the traditional ones, the authors’ framework can enable robots to simultaneously learn stiffness and the movement from human demonstrations. Additionally, Gaussian mixture model (GMM) is used to capture the features of movement and of stiffness from multiple demonstrations of the same skill. Human limb surface electromyography (sEMG) signals are estimated to obtain the reference stiffness profiles. The authors have experimentally demonstrated the effectiveness of the proposed framework. It shows that the authors approach could allow the robot to execute tasks in a variable impedance control mode with the learned movement trajectories and stiffness profiles. In robot skill acquisition, DMP is widely used to encode robotic behaviors. So far, however, these DMP modes do not provide the ability to properly represent and generalize stiffness profiles. The authors argue that both movement trajectories and stiffness profiles should be considered equally in robot skill learning. The authors’ approach has great potential of applications in the future hybrid manufacturing lines. 2. Neurodynamics-based leader-follower formation tracking of multiple nonholonomic vehicles Authors: Guo Yi, Jianxu Mao, Yaonan Wang, Hui Zhang, Zhiqiang Miao The purpose of this paper is to consider the leaderfollowing formation control problem for nonholonomic vehicles based on a novel biologically inspired neurodynamics approach. The interact[...]

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