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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Hongji Liang, Chengdong Wang, Shuai Chen, Zhengyang Zhang, Binbin Xiang, Xu Deng, Yanlei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Ai X-LANCE Lab Cse Dept Shanghai China Tencent Corporation Tencent Ethereal Audio Lab Shenzhen China WeNet Open Source Community Northwestern Polytechnical University School of Marine Science and Technology Xi'an China Nvidia Santa Clara United States The Chinese University of HongKong Shenzhen Research Institute of Big Data Shenzhen China Horizon Robotics Beijing China AISpeech Ltd Suzhou China

ISBN: (纸本)9781728163277

Speaker modeling is essential for many related tasks, such as speaker recognition and speaker diarization. The dominant modeling approach is fixed-dimensional vector representation, i.e., speaker embedding. This paper introduces a research and production oriented speaker embedding learning toolkit, Wespeaker. Wespeaker contains the implementation of scalable data management, state-of-the-art speaker embedding models, loss functions, and scoring back-ends, with highly competitive results achieved by structured recipes which were adopted in the winning systems in several speaker verification challenges. The application to other downstream tasks such as speaker diarization is also exhibited in the related recipe. Moreover, CPU- and GPU-compatible deployment codes are integrated for production-oriented development. The toolkit is publicly available at https://***/wenet-e2e/wespeaker. © 2023 IEEE.

关键词： Embeddings

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Wekws: A Production First Small-Footprint End-to-End Keyword Spotting Toolkit

Wekws: A Production First Small-Footprint End-to-End Keyword...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jie Wang Menglong Xu Jingyong Hou Binbin Zhang Xiao-Lei Zhang Lei Xie Fuping Pan School of Marine Science and Technology Northwestern Polytechnical University Xi’an China WeNet Open Source Community Horizon Robotics Beijing China Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xi’an China

Keyword spotting (KWS) enables speech-based user interaction and gradually becomes an indispensable component of smart devices. Recently, end-to-end (E2E) methods have be-come the most popular approach for on-device KWS tasks. However, there is still a gap between the research and deployment of E2E KWS methods. In this paper, we introduce WeKws, a production-quality, easy-to-build, and convenient-to-be-applied E2E KWS toolkit. WeKws contains the implementations of several state-of-the-art backbone networks, making it achieve highly competitive results on three publicly available datasets. To make WeKws a pure E2E toolkit, we utilize a refined max-pooling loss to make the model learn the ending position of the keyword by itself, which significantly simplifies the training pipeline and makes WeKws very efficient to be applied in real-world scenarios. The toolkit is publicly available at https://***/wenet-e2e/wekws.

关键词： Training Pipelines Production Signal processing Benchmark testing Acoustics Task analysis

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STEV: Stretchable Triboelectric E-skin enabled Proprioceptive Vibration Sensing for Soft Robot

STEV: Stretchable Triboelectric E-skin enabled Proprioceptiv...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zihan Wang Kai-Chong Lei Huaze Tang Shoujie Li Yuan Dai Wenbo Ding Xiao-Ping Zhang Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University China Tencent Robotics X Lab Shenzhen China RISC-V International Open Source Laboratory Shenzhen China Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto ON Canada

Vibration perception is essential for robotic sensing and dynamic control. Nevertheless, due to the rigorous demand for sensor conformability and stretchability, enabling soft robots with proprioceptive vibration sensing remains challenging. This paper proposes a novel liquid metal-based stretchable e-skin via a kirigami-inspired design to enable soft robot proprioceptive vibration sensing. The e-skin is fabricated into 0.1mm ultrathin thickness, ensuring its negligible influence on the overall stiffness of the soft robot. Moreover, the working mechanism of the e-skin is based on the ubiquitous triboelectrification effect, which transduces mechanical stimuli without external power supply. To demonstrate the practicability of the e-skin, we built a soft gripper consisting of three soft robotic fingers with proprioceptive vibration sensing. Our experiment shows that the gripper can accurately distinguish the grain category (six grains with the same mass, 99.9% accuracy) and the packaging quality (100% accuracy) by simply shaking the gripped bottle. In summary, a soft robotic proprioceptive vibration sensing solution is proposed; it helps soft robots to have a more comprehensive awareness of their self-state and may inspire further research on soft robotics.

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Optimal Motion Planning with the Half-Car Dynamical Model for Autonomous High-Speed Driving

Optimal Motion Planning with the Half-Car Dynamical Model fo...

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American Control Conference

作者： Jeong hwan Jeon Raghvendra V. Cowlagi Steven C. Peters Sertac Karaman Emilio Frazzoli Panagiotis Tsiotras Karl Iagnemma Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA 02139 Aurora Flight Sciences Corp Cambridge MA 02142 Open Source Robotics Foundation Mountain View CA 94043 School of Aerospace Engineering Georgia Institute of Technology Atlanta GA 30332-0150 Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02139

ISBN: (纸本)9781479901777

We discuss an implementation of the RRT* optimal motion planning algorithm for the half-car dynamical model to enable autonomous high-speed driving. To develop fast solutions of the associated local steering problem, we observe that the motion of a special point (namely, the front center of oscillation) can be modeled as a double integrator augmented with fictitious inputs. We first map the constraints on tire friction forces to constraints on these augmented inputs, which provides instantaneous, state-dependent bounds on the curvature of geometric paths feasibly traversable by the front center of oscillation. Next, we map the vehicle's actual inputs to the augmented inputs. The local steering problem for the half-car dynamical model can then be transformed to a simpler steering problem for the front center of oscillation, which we solve efficiently by first constructing a curvature-bounded geometric path and then imposing a suitable speed profile on this geometric path. Finally, we demonstrate the efficacy of the proposed motion planner via numerical simulation results.

关键词： motion planning kinetic model Steering Double integrators Curvature Constraint numerical simulation

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WESPEAKER: A RESEARCH AND PRODUCTION ORIENTED SPEAKER EMBEDDING LEARNING TOOLKIT

arXiv

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arXiv 2022年

作者： Wang, Hongji Liang, Chengdong Wang, Shuai Chen, Zhengyang Zhang, Binbin Xiang, Xu Deng, Yanlei Qian, Yanmin X-LANCE Lab Shanghai Jiao Tong University Shanghai China Tencent Ethereal Audio Lab Tencent Corporation Shenzhen China School of Marine Science and Technology Northwestern Polytechnical University Xi'An China Horizon Robotics Beijing China AISpeech Ltd Suzhou China NVIDIA Santa Clara United States WeNet Open Source Community China

关键词： Embeddings

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Path Generation for Wheeled Robots Autonomous Navigation on Vegetated Terrain

arXiv

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arXiv 2023年

作者： Jian, Zhuozhu Liu, Zejia Shao, Haoyu Wang, Xueqian Chen, Xinlei Liang, Bin The Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The School of Mechanical Engineering and Automation at Harbin Institute of Technology Shenzhen518055 China The Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Pengcheng Lab Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China

Wheeled robot navigation has been widely used in urban environments, but navigation in wild vegetation is still challenging. External sensors (LiDAR, camera etc.) are often used to construct point cloud map of the surrounding environment, however, the supporting rigid ground used for travelling cannot be detected due to the occlusion of vegetation. This often leads to unsafe or non-smooth paths during the planning process. To address the drawback, we propose the PE-RRT* algorithm, which effectively combines a novel support plane estimation method and sampling algorithm to generate real-time feasible and safe path in vegetation environments. In order to accurately estimate the support plane, we combine external perception and proprioception, and use Multivariate Gaussian Processe Regression (MV-GPR) to estimate the terrain at the sampling nodes. We build a physical experimental platform and conduct experiments in different outdoor environments. Experimental results show that our method has high safety, robustness and generalization. The source code is released for the reference of the community. Copyright © 2023, The Authors. All rights reserved.

关键词： Vegetation

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Wespeaker: A Research and Production Oriented Speaker Embedding Learning Toolkit

Wespeaker: A Research and Production Oriented Speaker Embedd...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hongji Wang Chengdong Liang Shuai Wang Zhengyang Chen Binbin Zhang Xu Xiang Yanlei Deng Yanmin Qian CSE Dept MoE Key Lab of AI X-LANCE Lab Shanghai Jiao Tong University Shanghai China Tencent Ethereal Audio Lab Tencent Corporation Shenzhen China WeNet Open Source Community School of Marine Science and Technology Northwestern Polytechnical University Xi’an China NVIDIA Santa Clara USA Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Horizon Robotics Beijing China AISpeech Ltd Suzhou China

关键词： Codes Production Signal processing Data models Acoustics Speaker recognition Task analysis

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When Vision Meets Touch: A Contemporary Review for Visuotactile Sensors from the Signal Processing Perspective

arXiv

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arXiv 2024年

作者： Li, Shoujie Wang, Zihan Wu, Changsheng Li, Xiang Luo, Shan Fang, Bin Sun, Fuchun Zhang, Xiao-Ping Ding, Wenbo Tsinghua Shenzhen International Graduate School Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore Centre for Robotics Research King’s College London LondonWC2R 2LS United Kingdom Department of Automation Tsinghua University Beijing100084 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Tactile sensors, which provide information about the physical properties of objects, are an essential component of robotic systems. The visuotactile sensing technology with the merits of high resolution and low cost has facilitated the development of robotics from environment exploration to dexterous operation. Over the years, several reviews on visuotactile sensors for robots have been presented, but few of them discussed the significance of signal processing methods to visuotactile sensors. Apart from ingenious hardware design, the full potential of the sensory system toward designated tasks can only be released with the appropriate signal processing methods. Therefore, this paper provides a comprehensive review of visuotactile sensors from the perspective of signal processing methods and outlooks possible future research directions for visuotactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal processing

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A Platypus-Inspired Electro-Mechanosensory Finger for Remote Control and Tactile Sensing

SSRN

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SSRN 2023年

作者： Ding, Wenbo Mu, Shilong Li, Shoujie Zhao, Hongfa Wang, Zihan Xiao, Xiao Lin, Zennan Song, Ziwu Tang, Huaze Xu, Qinghao Wang, Dongkai Lee, Wang Wei Wu, Changsheng Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Bioengineering China Institute for Health Innovation and Technology National University of Singapore Singapore117599 Singapore SIA-NUS Digital Aviation Corporate Lab National University of Singapore Singapore117602 Singapore Tencent Robotics X Shenzhen518054 China Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore RISC-V International Open Source Laboratory Shenzhen518055 China

Advancement in human-robot interaction (HRI) is essential for the development of intelligent robots, but there lack paradigms to integrate remote control and tactile sensing for an ideal HRI. In this study, inspired by the platypus beak sense, we propose a bionic electro-mechanosensory finger (EM-Finger) synergizing triboelectric and visuotactile sensing for remote control and tactile perception. A triboelectric sensor array made of a patterned liquid-metal-polymer conductive (LMPC) layer encodes both touchless and tactile interactions with external objects into voltage signals in the air, and responds to electrical stimuli underwater for amphibious wireless communication. Besides, a three-dimensional finger-shaped visuotactile sensing system with the same LMPC layer as a reflector measures contact-induced deformation through marker detection and tracking methods. A bioinspired bimodal deep learning algorithm implements data fusion of triboelectric and visuotactile signals and achieves the classification of 18 common material types under varying contact forces with an accuracy of 94.4%. The amphibious wireless communication capability of the triboelectric sensor array enables touchless HRI in the air and underwater, even in the presence of obstacles, while the whole system realizes high-resolution tactile sensing. By naturally integrating remote contorl and tactile sensing, the proposed EM-Finger could pave the way for enhanced HRI in machine intelligence. © 2023, The Authors. All rights reserved.

关键词： Human robot interaction

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