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Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

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arXiv 2024年

作者： Ou, Ni Chen, Zhuo Luo, Shan The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China The Robot Perception Lab. Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are availab.e at the project website https://***/Marker-Markerless-Transition/ Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

Revisiting Marr in Face: The Building of 2D-2.5D-3D Representations in Deep Neural Networks

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arXiv 2024年

作者： Zhu, Xiangyu Yu, Chang Zhao, Jiankuo Zhang, Zhaoxiang Li, Stan Z. Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China AI Lab. Research Center for Industries of the Future Westlake University China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong

David Marr’s seminal theory of vision proposes that the human visual system operates through a sequence of three stages, known as the 2D sketch, the 2.5D sketch, and the 3D model. In recent years, Deep Neural Networks (DNN) have been widely thought to have reached a level comparable to human vision. However, the mechanisms by which DNNs accomplish this and whether they adhere to Marr’s 2D–2.5D–3D construction theory remain unexplored. In this paper, we delve into the face perception task to explore these questions and find evidence supporting Marr’s theory. We introduce a graphics probe, a sub-network crafted to reconstruct the original face image from the network’s intermediate layers. The key to the graphics probe is its flexible architecture that supports image reconstruction in both 2D and 3D formats, as well as in a transitional state between them. By injecting graphics probes into neural networks, and analyzing their behavior in reconstructing images, we find that DNNs initially encode images as 2D representations in low-level layers, and finally construct 3D representations in high-level layers. Intriguingly, in mid-level layers, DNNs exhibit a hybrid state, building a geometric representation that captures surface normals within a narrow depth range, akin to the appearance of a low-relief sculpture. This stage resembles the 2.5D representations, providing a view of how DNNs evolve from 2D to 3D in the perception process. The graphics probe therefore serves as a tool for peering into the mechanisms of DNN, providing empirical support for Marr’s theory. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

Autonomous Water Sampling System and Experiment Based on the Unmanned Surface Vehicle

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Autonomous Water Sampling System and Experiment Based on the...

2021 IEEE International Conference on Unmanned Systems, ICUS 2021

作者： Zhang, Mengwei Li, Decai He, Yuqing Xiong, Junfeng Li, Zhi Xiong, Chunhong Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Guangzhou Institute of Industrial Intelligence Shenyang Institute of Automation Guangzhou Chinese Academy of Sciences Academicians Experts Workstation Guangzhou China Co. Ltd Natl. Eng. Lab. of Advanced Technology and Equipment for Water Environmental Pollution Monitoring Changsha China Co. Ltd Natl. Eng. Lab. of Advanced Technology and Equipment for Water Environmental Pollution Monitoring Changsha China

ISBN: (纸本)9780738146577

Water pollution is always the worldwide key problem. For the problem of complex, frequent and dangerous water sampling and monitoring missions in the various water areas, this paper designs an autonomous water sampling system based on the unmanned surface vehicle (USV), and it introduces the experimental application of the water sampling system. According to the water sampling missions, the ground station sets up the desired water sampling position, and then, the USV goes fleetly to the target position carrying the autonomous water sampling equipment to complete the mission by water sampling and control modules. This autonomous water sampling system based on the USV can long-periodic execute water sampling and monitoring missions precisely, safely and effectively. © 2021 IEEE.

关键词： Unmanned surface vehicles

Offboard Occupancy Refinement with Hybrid Propagation for Autonomous Driving

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arXiv 2024年

作者： Shi, Hao Wang, Song Zhang, Jiaming Yin, Xiaoting Wang, Zhongdao Wang, Guangming Zhu, Jianke Yang, Kailun Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany Huawei Noah’s Ark Lab. Canada The Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision. Previous methods, confined to onboard processing, struggle with simultaneous geometric and semantic estimation, continuity across varying viewpoints, and single-view occlusion. Our paper introduces OccFiner, a novel offboard framework designed to enhance the accuracy of vision-based occupancy predictions. OccFiner operates in two hybrid phases: 1) a multi-to-multi local propagation network that implicitly aligns and processes multiple local frames for correcting onboard model errors and consistently enhancing occupancy accuracy across all distances. 2) the region-centric global propagation, focuses on refining lab.ls using explicit multi-view geometry and integrating sensor bias, especially to increase the accuracy of distant occupied voxels. Extensive experiments demonstrate that OccFiner improves both geometric and semantic accuracy across various types of coarse occupancy, setting a new state-of-the-art performance on the SemanticKITTI dataset. Notably, OccFiner elevates vision-based SSC models to a level even surpassing that of LiDAR-based onboard SSC models. Furthermore, OccFiner is the first to achieve automatic annotation of SSC in a purely vision-based approach. Quantitative experiments prove that OccFiner successfully facilitates occupancy data loop-closure in autonomous driving. Additionally, we quantitatively and qualitatively validate the superiority of the offboard approach on city-level SSC static maps. The source code will be made publicly availab.e at https://***/MasterHow/OccFiner. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

Phase-Based Quantification of Sports Performance Metrics Using a Smart IoT Sensor

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IEEE Internet of Things Journal 2023年第18期10卷 15900-15911页

作者： Chen, Meng Szu, Hui Fang Lin, Hsin Yen Liu, Yifan Chan, Ho-Yin Wang, Yufan Zhao, Yuliang Zhang, Guanglie Yao, Jeffrey Da-Jeng Li, Wen Jung National Tsing Hua University Physical Education Office Hsinchu City300 Taiwan Kla Corporation Tainan City744 Taiwan Michigan State University Department of Electronic and Computer Engineering East LansingMI48824 United States Shanghai Jiao Tong University School of Mechanical Engineering Department of Industrial Engineering and Management Shanghai200240 China Northeastern University at Qinhuangdao Hebei Key Lab. of Micro-Nano Prec. Optical Sensing and Msrmt. Technol. Sch. of Control Engineering Qinhuangdao066004 China City University of Hong Kong Department of Mechanical Engineering Kowloon Tong Hong Kong City University of Hong Kong Shenzhen Research Institute CASŰCityU Joint Laboratory for Robotics Shenzhen518057 China National Tsing Hua University Department of Power Mechanical Engineering Hsinchu City300 Taiwan

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes' sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players' serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player's skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes' training. © 2014 IEEE.

关键词： Internet of things

Signal Feature Analysis of Contact Force at the Tip of a Flexible Ureteroscope 42

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Signal Feature Analysis of Contact Force at the Tip of a Fle...

42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Yinan, Deng Tangwen, Yang Shaotao, Dai Song, Guoli Beijing Jiaotong University Inst. of Info. Sci. and the Beijing Key Lab. of Advanced Information Science and Network Technology Beijing China Beijing Jiaotong University School of Electrical Engineering Beijing China Shenang Institute of Automation Cas State Key Laboratory of Robotics Shenyang China

ISBN: (纸本)9781728119908

This paper presents a signal analysis approach to identify the contact objects at the tip of a flexible ureteroscope. First, a miniature triaxial fiber optic sensor based on Fiber Bragg Grating(FBG) is devised to measure the interactive force signals at the ureteroscope tip. Due to the multidimensional properties of these force signals, the principal components analysis(PCA) method is introduced to reduce dimensions. The signal features are then extracted from the representative principal component signals using the wavelet transform(WT) method. Experimental results show that the contact objects at the tip of a ureteroscope are readily discriminated from the measured force signals with the proposed *** Relevance - This work commits to analyze the contact force signals at the tip of a flexible ureteroscope for the purpose of contact objects identification. © 2020 IEEE.

关键词： Fiber Bragg gratings

To root artificial intelligence deeply in basic science for a new generation of AI

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arXiv 2020年

作者： Yang, Jingan Peng, Yang Institute of AI and Robotics Hefei University of Technology Hefei230009 China State Key Lab. of Software Technology & Applications China

One of the ambitions of artificial intelligence is to root artificial intelligence deeply in basic science while developing brain-inspired artificial intelligence platforms that will promote new scientific discoveries. The challenges are essential to push artificial intelligence theory and applied technologies research forward. This paper presents the grand challenges of artificial intelligence research for the next 20 years which include: (i) to explore the working mechanism of the human brain on the basis of understanding brain science, neuroscience, cognitive science, psychology and data science;(ii) how is the electrical signal transmitted by the human brain? What is the coordination mechanism between brain neural electrical signals and human activities? (iii) to root brain-computer interface (BCI) and brain-muscle interface (BMI) technologies deeply in science on human behaviour;(iv) making research on knowledge-driven visual commonsense reasoning (VCR), develop a new inference engine for cognitive network recognition (CNR);(v) to develop high-precision, multi-modal intelligent perceptrons;(vi) investigating intelligent reasoning and fast decision-making systems based on knowledge graph (KG). We believe that the frontier theory innovation of AI, knowledge-driven modeling methodologies for commonsense reasoning, revolutionary innovation and breakthroughs of the novel algorithms and new technologies in AI, and developing responsible AI should be the main research strategies of AI scientists in the future. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

Mechanics of soft-body rolling motion without external torque

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arXiv 2024年

作者： Liang, Xudong Ding, Yimiao Yuan, Zihao Jiang, Junqi Xie, Zongling Fei, Peng Sun, Yixuan Gu, Guoying Zhong, Zheng Chen, Feifei Si, Guangwei Gong, Zhefeng School of Science Harbin Institute of Technology Shenzhen518055 China Department of Neurology The Fourth Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou310058 China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou311121 China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou310058 China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai200240 China Robotics Institute School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Brain and Cognitive Science Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing100101 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing100049 China Huazhong University of Science and Technology Optical and Electronic Information Wuhan430074 China Zhejiang Lab. Hangzhou311121 China

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we constructed a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics. © 2024, CC BY.

关键词： Pneumatics

Single focused-beam acoustical tweezers: Trapping cells in 3‐D

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The Journal of the Acoustical Society of America 2023年第4_SUPPLEMENT期154卷 A263-A263页

作者： Zhixiong Gong Shiyu Li Zhichao Ma Key Lab. of Marine Intelligent Equipment and System State Key Lab. of Ocean Eng. School of Naval Architecture Ocean and Civil Eng. Shanghai Jiao Tong Univ. Dongchuan Rd. #800 Minhang District Shanghai 200240 China zhixiong.gong@*** Key Lab. of Marine Intelligent Equipment and System State Key Lab. of Ocean Eng. School of Naval Architecture Ocean and Civil Eng. Shanghai Jiao Tong Univ. Shanghai China Inst. of Medical Robotics School of Biomedical Eng. Shanghai Jiao Tong Univ. Shanghai China

Single focused beams have shown great potential in acoustofluidics and medical ultrasound because of their large pressure gradient and acoustic-thermal effect, such as acoustical tweezers and high-intensity focused ultrasound (HIFU) technique. However, the three-dimensional trapping of typical cells is still challenging since cells in water have a positive acoustic contrast factor and will be repulsive from the focus [Gong and Baudoin, Phys. Rev. Applied 18, 044033 (2022)]. In this work, to reverse this positive acoustic contrast factor into negative, we propose to use a cell-friendly medium (made of iodixanol with water) [Augustsson et al., Nat. Commun. 7, 11556 (2016)]. This enables a single focused beam to selectively trap and control cells in 3‐D, and more importantly, keeps the manipulated cells with good viability. This work will extend the applications of acoustical tweezers, which may be beneficial to single cell analysis, cellular phenotyping, precise assembly of different cells in tissue engineering, and controlled drug delivery.

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