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Human-robot interaction has received a lot of attention as collaborative robots became widely utilized in many industrial fields. Among techniques for human-robot interaction, collision identification is an indispensable element in collaborative robots to prevent fatal accidents. This paper proposes a deep learning method for identifying external collisions in 6-DoF articulated robots. The proposed method expands the idea of CollisionNet, which was previously proposed for collision detection, to identify the locations of external forces. The key contribution of this paper is uncertainty-aware knowledge distillation for improving the accuracy of a deep neural network. Sample-level uncertainties are estimated from a teacher network, and larger penalties are imposed for uncertain samples during the training of a student network. Experiments demonstrate that the proposed method is effective for improving the performance of collision identification.

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This paper deals with the synchronization of fuzzy neural networks (FNNs) with time-varying delays. FNNs are more complicated form of neural networks incorporated with fuzzy logics, which provide more powerful performances. Especially, the problem of delayed FNNs's synchronization is of importance in the existence of the network communication. For the synchronization of FNNs with time-varying delays, a novel form of control structure is proposed employing affinely transformed membership functions with memory element. In accordance with affine memory control, appropriate Lyapunov-Krasovskii functional is chosen to design control gain, guaranteeing stability of the systems with delays. Exploiting the more general type of control attributed by affine transformation and memory-type, a novel criterion is derived in forms of linear matrix inequalities (LMIs). As a results, the effectiveness of the proposed control is shown through numerical examples by comparisons with others.