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Automating Care by Self-maintainability for Full Laboratory Automation

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arXiv 2025年

作者： Ochiai, Koji Tahara-Arai, Yuya Kato, Akari Kaizu, Kazunari Kariyazaki, Hirokazu Umeno, Makoto Takahashi, Koichi Kanda, Genki N. Ozaki, Haruka Laboratory for Biologically Inspired Computing RIKEN Center for Biosystems Dynamics Research 6-7-1 Minatojima Minamimachi Chuo-ku Hyogo Kobe650-0047 Japan Bioinformatics Laboratory Institute of Medicine University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Ph.D. Program in Humanics School of Integrative and Global Majors University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Laboratory Automation Suppliers’ Association 2-1-8 Minatojima Minamimachi Chuo-ku Hyogo Kobe650-0047 Japan Cell Modeling and Simulation Group Exploratory Research Center on Life and Living Systems National Institutes of Natural Sciences 5-1 Higashiyama Myodaiji Aichi Okazaki444-8787 Japan Robotics Division YASKAWA Electric Corporation 2-1 Kurosaki Shiroishi Yahatanishi-ku Kitakyushu Fukuoka806-0004 Japan BAIKEIDO LLC 3-8-1 Asano Kokurakita-ku Kitakyushu Fukuoka802-0001 Japan Graduate School of Media and Governance Keio University 5322 Endo Fujisawa-shi Kanagawa252-0882 Japan Medical Research Laboratory Institute of Integrated Research Institute of Science Tokyo 1-5-45 Yushima Bunkyo-ku Tokyo113-8519 Japan Center for Artificial Intelligence Research University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan

The automation of experiments in life sciences and chemistry has significantly advanced with the development of various instruments and AI technologies. However, achieving full laboratory automation, where experiments conceived by scientists are seamlessly executed in automated laboratories, remains a challenge. We identify the lack of automation in planning and operational tasks—critical human-managed processes collectively termed "care"—as a major barrier. Automating care is the key enabler for full laboratory automation. To address this, we propose the concept of self-maintainability (SeM): the ability of a laboratory system to autonomously adapt to internal and external disturbances, maintaining operational readiness akin to living cells. A SeM-enabled laboratory features autonomous recognition of its state, dynamic resource and information management, and adaptive responses to unexpected conditions. This shifts the planning and execution of experimental workflows, including scheduling and reagent allocation, from humans to the system. We present a conceptual framework for implementing SeM-enabled laboratories, comprising three modules—Requirement manager, Labware manager, and Device manager—and a Central manager. SeM not only enables scientists to execute envisioned experiments seamlessly but also provides developers with a design concept that drives the technological innovations needed for full automation. Copyright © 2025, The Authors. All rights reserved.

关键词： Information management

Frequency dependent reduction of cyber-sickness nausea in Virtual Reality by transcranial oscillatory stimulation of the vestibular cortex

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Brain Stimulation 2023年第1期16卷 260-260页

作者： Benelli, Alberto Neri, Francesco Cinti, Alessandra Romanella, Sara M. Giannotta, Alessandro De Monte, David Mandalà, Marco Smeralda, Carmelo Prattichizzo, Domenico Rossi, Simone Santarnecchi, Emiliano Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab) Unit of Neurology and Clinical Neurophysiology Department of Medicine Surgery and Neuroscience University of Siena Italy Oto-Neuro-Tech Conjoined Lab Policlinico Le Scotte University of Siena Italy Precision Neuroscience & Neuromodulation Program Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Boston MA USA Otolaryngology Department of Medicine Surgery and Neuroscience Neurology and Clinical Neurophysiology Section University of Siena Italy Siena Robotics and Systems (SiRS) Lab Department of Information Engineering and Mathematics University of Siena Siena Italy

Using Bacteria Foraging Optimization in Gesture Reconfiguration from Joint Failure for Semi-Humanoid Robot

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Using Bacteria Foraging Optimization in Gesture Reconfigurat...

International Conference on Control, Automation and Systems ( ICCAS)

作者： Wisanu Jutharee Boonserm Kaewkamnerdpong Jumpol Polvichai Thavida Maneewarn Institute of Field Robotics King Mongkut's University of Technology Thonburi Bangkok Thailand Biological Engineering Program Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

Joint failure problem in humanoid robots could often occur in real application. An approach for fault recovery is performing gesture reconfiguration on a redundant manipulator of humanoid robots. In this study, we proposed a gesture reconfiguration method based on bacteria foraging optimization algorithm. After joint failure, we locked the failed joint in place and used the BFOA-based gesture reconfiguration to find a new suitable gesture configuration for the remaining joints. We demonstrated the gesture reconfiguration from joint failure on the Namo robot, which is a semi-humanoid robot for performing emblematic gestures as a receptionist robot. Four emblematic gestures, including the Thai greeting, salute, bye, and side invite gestures, were demonstrated. The experimental results showed that the BFOA-based gesture reconfiguration could generate a suitable gesture configuration for all four gestures; the resulting gestures are close to the original gestures. We investigated further on tuning the number of population size in BFOA. We varied the number of population size as 15, 25, 50, and 100 and found that although the larger population size could result in better gestures than the smaller size, the improvement in gesture similarity were not significant so it may be more efficient to use the smaller size. This could be owing to the performance of bacteria foraging optimization algorithm.

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Decision-Level Fusion Method for Emotion Recognition using Multimodal Emotion Recognition Information 15

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Decision-Level Fusion Method for Emotion Recognition using M...

15th International Conference on Ubiquitous Robots, UR 2018

作者： Song, Kyu-Seob Nho, Young-Hoon Seo, Ju-Hwan Kwon, Dong-Soo Department of Robotics Program Korea Advanced Institute of Science and Technology Human-Robot Interaction Research Center Korea Republic of Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Human-Robot Interaction Research Center Korea Republic of

ISBN: (纸本)9781538663349

Human emotion recognition is an important factor for social robots. In previous research, emotion recognizers with many modalities have been studied, but there are several problems that make recognition rates lower when a recognizer is applied to a robot. This paper proposes a decision level fusion method that takes the outputs of each recognizer as an input and confirms which combination of features achieves the highest accuracy. We used EdNet, which was developed in KAIST based Convolutional Neural Networks (CNNs), as a facial expression recognizer and a speech analytics engine developed for speech emotion recognition. Finally, we confirmed a higher accuracy 43.40% using an artificial neural network (ANN) or the k-Nearest Neighbor (k-NN) algorithm for classification of combinations of features from EdN et and the speech analytics engine. © 2018 IEEE.

关键词： Engines

RRNet: Repetition-reduction network for energy efficient decoder of depth estimation

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arXiv 2019年

作者： Oh, Sangyun Kim, Hye-Jin S. Lee, Jongeun Kim, Junmo School of Electrical and Computer Engineering UNIST Ulsan Korea Republic of Neural Processing Research Center Seoul National University Seoul Korea Republic of Robotics Program School of Electrical Engineering KAIST Daejeon Korea Republic of Artificial Intelligence Laboratory ETRI Daejeon Korea Republic of

We introduce Repetition-Reduction network (RRNet) for resource-constrained depth estimation, offering significantly improved efficiency in terms of computation, memory and energy consumption. The proposed method is based on repetition-reduction (RR) blocks. The RR blocks consist of the set of repeated convolutions and the residual connection layer that take place of the pointwise reduction layer with linear connection to the decoder. The RRNet help reduce memory usage and power consumption in the residual connections to the decoder layers. RRNet consumes approximately 3.84 times less energy and 3.06 times less memory and is approaximately 2.21 times faster, without increasing the demand on hardware resource relative to the baseline network [1], outperforming current state-of-the-art lightweight architectures such as SqueezeNet [2], ShuffleNet [3], MobileNetv2 [4] and PyDNet [5]. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy utilization

The agile robotics for industrial automation competition

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The agile robotics for industrial automation competition

作者： Harrison, William Downs, Anthony Schlenoff, Craig NIST Intelligent Systems Division United States Robotics Systems for Smart Manufacturing Program Agility Performance of Robotic Systems Cognition and Collaboration Systems Group Intelligent Systems Division National Institute of Standards and Technology United States United States

Evaluation of an adjustable support shoulder exoskeleton on static and dynamic overhead tasks

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2018年第1期62卷 804 - 808页

作者： Logan Van Engelhoven Nathan Poon Homayoon Kazerooni Alan Barr David Rempel Carisa Harris-Adamson University of California Berkeley Human Engineering and Robotics Lab University of California Berkeley/San Francisco Ergonomics Graduate Research Training Program

Introduction: Overhead tasks increase the risk of work related musculoskeletal disorders to industrial workers. A shoulder supporting exoskeleton with adjustable and angle dependent torque (referred to as shoulderX in this paper for brevity) was designed and built at the University of California Berkeley Human Engineering and robotics Laboratory for workers performing overhead tasks. shoulderX was designed specifically to reduce the exposure to large muscle exertion forces on the shoulder complex from overhead work. Methods: We evaluated shoulderX by measuring the muscle activation of the upper trapezius (UT), anterior deltoid (AD), triceps long head (TR), and infraspinatus (IF) during static and dynamic overhead tasks. Thirteen male subjects with experience in the construction or manufacturing industries were recruited to perform overhead tasks using light (.45 kg) and heavy (2.25 kg) weight tools with four exoskeleton support levels (0, 8.5, 13.0, 20.0 Nm peak torque). Results: During all conditions, the wearer’s shoulder flexor muscle activity of UT, AD were reduced with increasing strength of shoulderX by up to 80%. Subjects unanimously preferred the use of shoulderX over the unassisted condition for all task types (static and dynamic overhead tasks) and tool weights (.45 kg and 2.25 kg). Conclusion: shoulderX reduces the wearer’s primary muscle activity in overhead static and dynamic work and results in a more desirable and balanced pattern of shoulder complex activation. This investigation indicates that shoulderX reduces the risk of work related shoulder injuries during overhead tasks.

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Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition

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arXiv 2018年

作者： Liu, Xuan Fu, Jie Department of Electrical and Computer Engineering Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA United States

In hierarchical planning for Markov decision processes (MDPs), temporal abstraction allows planning with macro-actions that take place at different time scale in form of sequential composition. In this paper, we propose a novel approach to compositional reasoning and hierarchical planning for MDPs under co-safe temporal logic constraints. In addition to sequential composition, we introduce a composition of policies based on generalized logic composition: Given sub-policies for sub-tasks and a new task expressed as logic compositions of subtasks, a semi-optimal policy, which is optimal in planning with only sub-policies, can be obtained by simply composing sub-polices. Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying constraints specified in the fragment of co-safe temporal logic. We demonstrate the correctness and efficiency of the proposed method in stochastic planning examples with a single agent and multiple task specifications. Copyright © 2018, The Authors. All rights reserved.

关键词： Markov processes

Positive pressure testing booths development and deployment in response to the COVID-19 outbreak

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arXiv 2021年

作者： Aroom, Kevin Ge, Jiawei White, Marcee Al-Zogbi, Lidia Trustman, Adrienne Greenbaum, Adena Krieger, Axel Farley, Jason Robert E. Fischell Institute for Biomedical Devices Department of Bioengineering University of Maryland College ParkMD20742 United States Laboratory for Computational Sensing and Robotics Department of Mechanical Engineering Johns Hopkins University BaltimoreMD21218 United States Robert E. Fischell Institute for Biomedical Devices Department of Mechanical Engineering University of Maryland College ParkMD20742 United States Total Health Care Inc. 1501 Division Street BaltimoreMD21217 United States Health Care for the Homeless 421 Fallsway BaltimoreMD21202 United States STI/HIV Program Baltimore City Health Department BaltimoreMD21202 United States The REACH Initiative Johns Hopkins University School of Nursing BaltimoreMD21205 United States

The COVID-19 pandemic left an unprecedented impact on the general public health, resulting in thousands of deaths in the US alone. Nationwide testing plans were initiated to control the spread, with drive-through being the currently dominant testing approach, which, however, exhausts personal protective equipment supplies, and is unfriendly to individuals not owning a vehicle. Walk-up testing booths are a safe alternative, but are too prohibitively priced on the market to allow for nationwide deployment. In this paper, we present an accessible, mobile, affordable, and safe version of a positive-pressure COVID-19 testing booth. The booths are manufactured using primarily off-the-shelf components from US vendors with minimized customization. The booths’ mobility allows them to be easily transported within local communities to test a larger subset of the population with fewer transportation options. Moreover, the final bill of materials does not surpass USD 3,900, which is about half of the market price. The booths are air conditioned and HEPA filtered to offer healthcare providers a safe and comfortable working environment. The prototype passed required pressure and air exchange tests, and was positively reviewed by two healthcare professionals. Currently, five booths are deployed and used at the Johns Hopkins University School of Nursing, Baltimore City Health Department, and two community health centers in Baltimore. Our design facilitates walk-up testing in the US, as it decreases PPE consumption;reduces the risk of infection;and is accessible to lower-income communities and non-drivers. © 2021, CC BY-SA.

关键词： Air conditioning