Enhanced teleoperation performance using hybrid control and virtual fixture

To develop secure, natural and effective teleoperation, the perception of the slave plays a key role for the interaction of a human operator with the environment. By sensing slave information, the human operator can choose the correct operation in a process during the human–robot interaction.This paper develops an integrated scheme based on a hybrid control and virtual fixture approach for the telerobot. The human operator can sense the slave interaction condition and adjust the master device via the surface electromyographic signal. This hybrid control method integrates the proportional-derivative control and the variable stiffness control, and involves the muscle activation at the same time. It is proposed to quantitatively analyse the human operator’s control demand to enhance the control performance of the teleoperation system. In addition, due to unskilful operation and muscle physiological tremor of the human operator, a virtual fixture method is developed to ensure accuracy of operation and to reduce the operation pressure on the human operator. Experimental results demonstrated the effectiveness of the proposed method for the teleoperated robot

FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings

Logo embedding plays a crucial role in various e-commerce applications by facilitating image retrieval or recognition, such as intellectual property protection and product search. However, current methods treat logo embedding as a purely visual problem, which may limit their performance in real-world scenarios. A notable issue is that the textual knowledge embedded in logo images has not been adequately explored. Therefore, we propose a novel approach that leverages textual knowledge as an auxiliary to improve the robustness of logo embedding. The emerging Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in both visual and textual understanding and could become valuable visual assistants in understanding logo images. Inspired by this observation, our proposed method, FashionLOGO, aims to utilize MLLMs to enhance fashion logo embedding. We explore how MLLMs can improve logo embedding by prompting them to generate explicit textual knowledge through three types of prompts, including image OCR, brief captions, and detailed descriptions prompts, in a zero-shot setting. We adopt a cross-attention transformer to enable image embedding queries to learn supplementary knowledge from textual embeddings automatically. To reduce computational costs, we only use the image embedding model in the inference stage, similar to traditional inference pipelines. Our extensive experiments on three real-world datasets demonstrate that FashionLOGO learns generalized and robust logo embeddings, achieving state-of-the-art performance in all benchmark datasets. Furthermore, we conduct comprehensive ablation studies to demonstrate the performance improvements resulting from the introduction of MLLMs

[2-(3,5-Dimethyl-1H-pyrazol-1-yl-κN 2)-1,10-phenanthroline-κ2 N,N′]bis­(thio­cyanato-κN)cadmium(II)

In the title complex, [Cd(NCS)2(C17H14N4)], the CdII ion is in a distorted trigonal-bipyramidal CdN5 coordination geometry. In the crystal structure, there is a π–π stacking inter­action involving a pyrazole ring and a symmetry-related pyridine ring with a centroid–centroid distance of 3.578 (3) Å

A task learning mechanism for the telerobots

Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challengeable to exploit such systems to implement complex tasks in an autonomous way. In this paper, we propose a task learning framework to represent the manipulation skill demonstrated by a remotely controlled robot.Gaussian mixture model is utilized to encode and parametrize the smooth task trajectory according to the observations from the demonstrations. After encoding the demonstrated trajectory, a new task trajectory is generated based on the variability information of the learned model. Experimental results have demonstrated the feasibility of the proposed method