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arXiv 2023年

作者： Saito, Namiko Hiramoto, Mayu Kubo, Ayuna Suzuki, Kanata Ito, Hiroshi Sugano, Shigeki Ogata, Tetsuya Department of Modern Mechanical Engineering Waseda University Tokyo Japan The School of Informatics The University of Edinburgh Edinburgh United Kingdom The Alan Turing Institute London United Kingdom The Artificial Intelligence Laboratories Fujitsu Limited Kanagawa Japan The Center for Technology Innovation - Controls and Robotics Research & Development Group Hitachi Ltd. Ibaraki Japan The Department of Intermedia Art and Science Waseda University Tokyo Japan The National Institute of Advanced Science and Technology Tokyo Japan

To support humans in their daily lives, robots are required to autonomously learn, adapt to objects and environments, and perform the appropriate actions. We tackled on the task of cooking scrambled eggs using real ingredients, in which the robot needs to perceive the states of the egg and adjust stirring movement in real time, while the egg is heated and the state changes continuously. In previous works, handling changing objects was found to be challenging because sensory information includes dynamical, both important or noisy information, and the modality which should be focused on changes every time, making it difficult to realize both perception and motion generation in real time. We propose a predictive recurrent neural network with an attention mechanism that can weigh the sensor input, distinguishing how important and reliable each modality is, that realize quick and efficient perception and motion generation. The model is trained with learning from the demonstration, and allows the robot to acquire "human-like" skills. We validated the proposed technique using the robot, Dry-AIREC, and with our learning model, it could perform cooking eggs with unknown ingredients. The robot could change the method of stirring and direction depending on the status of the egg, as in the beginning it stirs in the whole pot, then subsequently, after the egg started being heated, it starts flipping and splitting motion targeting specific areas, although we did not explicitly indicate them. See our accompanying video here: https://***/PHHMDUO0mHU Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

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Novel Methods Inspired by Reinforcement Learning Actor-Critic Mechanism for Eye-in-Hand Calibration in robotics

Novel Methods Inspired by Reinforcement Learning Actor-Criti...

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IEEE International Conference on development and Learning, ICDL

作者： Chenxing Li Yinlong Liu Yingbai Hu Fabian Schreier Jan Seyler Shahram Eivazi Department of Computer Science University of Tübingen Tübingen Germany Advanced Development Analytics and Control Festo SE and Co. KG Esslingen Germany State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) University of Macau Macau China HUA Cloud Intelligent Healthcare Co. Ltd. Ningbo China Multi-Scale Medical Robotics Centre and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong China

Eye-in-hand camera calibration is an essential step in robotic vision systems. Traditional methods perform the calibration via geometry computation methods where the calibration points are acquired by moving the robot arm while capturing images of a static object (often a checkerboard) of known geometry. Our work aims to explore deep reinforcement learning (DRL) techniques for solving common calibration optimization problems. Inspired by the actor-critic mechanism of DRL, we propose a novel approach to perform eye-in-hand camera calibration on the robot arm. Numerical and real-world experiments prove the feasibility of the proposed method as such here we show the potential of the reinforcement learning method to solve the camera calibration problem.

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Social Presence in Evaluations for a Humanoid Robot and Its Effect on Children-Robot Relationship 23rd

Social Presence in Evaluations for a Humanoid Robot and Its ...

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23rd International Conference on Human-Computer Interaction, HCII 2021

作者： Chen, Yi-Chen Gamborino, Edwinn Fu, Li-Chen Yueh, Hsiu-Ping Yeh, Su-Ling Department of Psychology National Taiwan University Taipei Taiwan Department of Electrical Engineering and Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan MOST Joint Research Center for AI Technology and All Vista Healthcare Taipei Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan Department of Bio-Industry Communication and Development National Taiwan University Taipei Taiwan Graduate Institute of Brain and Mind Sciences National Taiwan University Taipei Taiwan

ISBN: (纸本)9783030786342

As children are at a critical stage in their lives in developing and practicing social skills, they tend to imagine objects as interactive agents. For example, even a toy dinosaur can be perceived as affable and more as a social entity, namely, with a strong social presence (SP). This study investigated whether perceived SP changes (measured using the SP questionnaire) as children interacted with a robot. Furthermore, we investigated how SP modulated children-robot relationships and children’s overall attitudes towards robots. Children aged 6–12 interacted with a companion robot, RoBoHoN, for short intervals. Attitude evaluations were completed at three-time points: prior-, mid-, and post-interaction. SP and rapport with RoBoHoN were measured and compared mid-and post-interaction, while attitudes towards the robot were tested prior- and post-interaction. The results showed a stronger intention to use robots before interaction correlated with higher SP scores with RoBoHoN. As SP level was stable across mid- and post-interaction, we analyzed SP as a between-groups factor to compare attitude evaluations among children who rated RoBoHoN with higher versus lower SP scores. The high SP group found RoBoHoN to be more likable and playing with it as more enjoyable, but the effect on rapport level was marginal. The Implicit Association Task revealed no bias towards robots among children in both groups. This study shows SP’s positive effect on children-robot interaction and highlights the importance of SP in designing social companion robots for children. © 2021, Springer Nature Switzerland AG.

关键词： Association reactions

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Inertial Measurement Unit to Segment Calibration Based on Physically Constrained Pose Generation

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SICE Journal of Control, Measurement, and System Integration 2020年第3期13卷 122-130页

作者： Maruyama, Tsubasa Toda, Haruki Ishii, Wataru Tada, Mitsunori Japan Artificial Intelligence Research Center AIST Japan Robotics Applied Research Group Advanced Technology Research Division Research & Development Section Advanced Technology Center Yamaha Motor Co. Ltd. Fukuroi Engineering Center Japan

The accuracy of a motion capture (MoCap) system based on inertial measurement units (IMUs) depends on the IMU-to-segment (I2S) calibration, in which the IMU alignment relative to a body segment is calculated using a reference pose such as a standing T-pose. This study proposes a novel I2S calibration system for a reliable MoCap system. In this system, the reference pose for calibration is constrained by physical objects, and this pose is generated by an optimization-based method which incorporates the IMU measurements with the physical constraints between the body and the object. To demonstrate the system, we estimated the chair-sitting and half-squat motions based on the calibration using a chair and estimated the center-of-mass movements of a rider when riding a motorcycle based on the calibration using a motorcycle. The experiments confirmed the improvements of the motion estimation accuracy by the proposed system, in both chair-sitting and half-squat motion and center-of-mass tracking of a rider. Furthermore, the proposed system enables the I2S calibration using product-use poses. © Taylor & Francis Group, LLC 2020.

关键词： Calibration

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Overcoming Van der Waals Forces in reconfigurable nanostructures

arXiv

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arXiv 2022年

作者： Zhang, Wang Wang, Hao Tan, Alvin T.L. Ranganath, Anupama Sargur Zhang, Biao Wang, Hongtao Chan, John You En Ruan, Qifeng Liu, Hailong Ha, Son Tung Wang, Dong Ravikumar, Venkat K. Low, Hong Yee Yang, Joel K.W. Engineering Product Development Singapore University of Technology and Design Singapore487372 Singapore Northwestern Polytechnical University 127 West Youyi Road Xi'An710072 China Institute of Materials Research and Engineering Singapore138634 Singapore Robotics Institute State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Advanced Micro Devices Singapore Pte Ltd Singapore Singapore

Reconfigurable metamaterials1-3 require constituent nanostructures to demonstrate switching of shapes with external stimuli. For generality, such nanostructures would touch and stick to other surfaces in one of its configurations. Yet, a longstanding challenge is in overcoming this stiction caused by Van der Waals forces, which impedes shape recovery. Here, we introduce a stiff yet self-recovering material system based on acrylic acid, and tested it in high-aspect ratio structures, where recovery is weak. This designer material has a storage modulus of ~5.2 GPa at room temperature and ~90 MPa in the rubbery state at 150 ℃, an order of magnitude higher than previous reports. A high-resolution resin for two-photon lithography was developed based on this polymer system, enabling 3D printing of nanopillars with diameters of ~400 nm and aspect ratio as high as ~10. Experimentally, we observed self-recovery as collapsed and touching structures overcome stiction to stand back up. We developed a theoretical model to explain the recoverability of these sub-micron structures. Reconfigurable structural colour prints and holograms were demonstrated, indicating potential applications of the material system as a shape memory polymer suitable for sub-micron reconfigurable metamaterials. Copyright © 2022, The Authors. All rights reserved.

关键词： Van der Waals forces

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Detection and Location Estimation of Object in Unmanned Aerial Vehicle using Single Camera and GPS

Detection and Location Estimation of Object in Unmanned Aeri...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Snehil Sanyal Shashank Bhushan K Sivayazi Department of Mechanical Engineering Defence Institute of Advanced Technology Pune India Center for Artificial Intelligence & Robotics (CAIR) Defence Research & Development Organization (DRDO) Bengaluru India

ISBN: (数字)9781728149974

ISBN: (纸本)9781728149981

It is important for an Unmanned Aerial Vehicle (UAV) to detect any object in its view. This enables the UAV to locate the object with respect to itself and is required for locking and tracking the object. Object detection and location estimation makes the UAV capable of manipulating the environment as well as to follow the target. In the present work, object detection has been carried out using You Only Look Once (YOLO) to detect the object in the image stream of the Robot Operating System (ROS) bag file. The GPS information of the UAV is used to further calculate the GPS coordinates of the object. The images are acquired using a single monocular camera.

关键词： Cameras Object detection Global Positioning System Drones Three-dimensional displays Estimation

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Learning-Based Keypoint Registration for Fetoscopic Mosaicking

arXiv

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arXiv 2022年

作者： Casella, Alessandro Bano, Sophia Vasconcelos, Francisco David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women’s Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto "Giannina Gaslini" Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Italy

In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. In the current practice, TTTS is treated surgically by closing abnormal anastomoses using laser ablation. This surgery is minimally invasive and relies on fetoscopy. Limited field of view makes anastomosis identification a challenging task for the surgeon. To tackle this challenge, we propose a learning-based framework for in-vivo fetoscopy frame registration for field-of-view expansion. The novelties of this framework relies on a learning-based keypoint proposal network and an encoding strategy to filter (i) irrelevant keypoints based on fetoscopic image segmentation and (ii) inconsistent homographies. We validate of our framework on a dataset of 6 intraoperative sequences from 6 TTTS surgeries from 6 different women against the most recent state of the art algorithm, which relies on the segmentation of placenta vessels. The proposed framework achieves higher performance compared to the state of the art, paving the way for robust mosaicking to provide surgeons with context awareness during TTTS surgery. Copyright © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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Does Speaking Training Application with Speech Recognition Motivate Junior High School Students in Actual Classroom? - A Case Study 8

Does Speaking Training Application with Speech Recognition M...

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8th ISCA Workshop on Speech and Language Technology in Education, SLaTE 19

作者： Kobashikawa, Satoshi Odakura, Atushi Nakamura, Takao Mori, Takeshi Endo, Kimitaka Moriya, Takafumi Masumura, Ryo Aono, Yushi Minematsu, Nobuaki NTT Media Intelligence Laboratories NTT Corporation Japan Head of Research and Development Planning NTT Corporation Japan AI and Robotics Business Headquarters NTT Advanced Technology Corporation Japan Graduate School of Engineering The University of Tokyo Japan

This paper investigates the effectiveness of a speech training application with question answering problems based on speech recognition which is robust to noise in a classroom. In actual classrooms, since a lot of students speak at the same time, input speech waveforms include speech noise from neighboring students. To the best of our knowledge, no speech training study has examined speech recognition with the focus on actual noise corrupted speech input in classrooms. Since existing mobile applications assumed solitary voice input, they failed to evaluate the input as corrupted by speech noise. To maintain students' motivation even in noisy environments, our application ignores the insertion errors around the user's intended sentence. To adapt to junior high school students' speech and the background speech noise, we introduce unsupervised adaption with matched sentences by comparing the speech recognition results and target sentences candidates. We also improve the user interface by reflecting the feedback from teachers and students. The results of a two month trial with over 140 students in a public junior high school show that our speech recognizer improves accuracy and our application achieves a positive user experience. © SLaTE 2019. All rights reserved.

关键词： Speech recognition

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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Assist Timing Decision Method for Wire Type Walking Assist Suit by Hip Joint Angular Acceleration

Assist Timing Decision Method for Wire Type Walking Assist S...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Junyuan Zhang Hiroumi Murai Akihito Ito Nobutaka Tsujiuchi Tsuyoshi Inoue Kenta Murakami Fumiya Hanzawa Kazuaki Kishimoto Jun Ozawa Faculty of Science and Engineering Doshisha University Kyoto Japan Faculty of Robotics & Design (of Affiliation) Osaka Institute of Technology Osaka Japan Living Innovation Division Panasonic Corporation Tokyo Japan Technical Development Div. ATOUN Inc. Nara Japan Panasonic-AIST Advanced AI Research Laboratory National Institute of Advanced Industrial Science and Technology Tsukuba Japan

ISBN: (数字)9781728198026

ISBN: (纸本)9781728198033

In this study, we proposed a method to determine the assist timing for wire type assist suit. In this method, since the assist timing is determined based on the hip joint angular acceleration by the IMU sensor, the assist can be performed at the optimal timing for each user. As a result of the experiment that in some trials, the maximum hip extension torque were reduced compared to normal walking can be observed. That is the effectiveness of this method can be expected.

关键词： Legged locomotion Torque Wires Timing Acceleration Hip Consumer electronics

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