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12th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017

作者： Rueben, Matthew Smart, William D. Grimm, Cindy M. Cakmak, Maya Robotics Program Oregon State University CorvallisOR97331 United States Computer Science and Engineering University of Washington SeattleWA98195 United States

ISBN: (纸本)9781450348850

As robots start to enter our everyday lives, they will bring with them the risk of privacy invasions. Unlike videoconferencing, we might not have control of where the sensors on our robots look, and where the robots go. They might operating in or homes and offices while we are not there, opening the door to an invasion of our privacy that no previous technology has been capable of. How do people think about privacy in terms of robots? Are they worried about their privacy being violated and, if so, in what ways? How does the law view the issues around privacy and robotics? What are the pressing questions that we need to address now, before it's too late. This workshop will bring together researchers from a wide variety of communities to look at these questions, identify others, and help define the new area of Privacy-Sensitive robotics. The workshop will result in a white paper that defines the pressing issues in Privacy-Sensitive robotics, from a variety of perspectives, and will suggest a research agenda to address these problems. © 2017 Authors.

关键词： Human robot interaction

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Association Between Air Pollution and Obstructive Sleep Apnea in Terms of Respiratory Event Frequency and Lung-to-Finger Circulation Time

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Atmospheric Pollution Research 2025年

作者： Rachel Chien Zhihe Chen Peizheng Wang Wen-Te Liu Ying-Ying Chen Yen-Ling Chen Arnab Majumdar Jiunn-Horng Kang Kang-Yun Lee Wun-Hao Cheng Yi-Chih Lin Cheng-Jung Wu Yi-Chun Kuan Hsin-Chien Lee Cheng-Yu Tsai Research Center of Sleep Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Civil and Environmental Engineering Imperial College London London SW7 2AZ United Kingdom School of Respiratory Therapy College of Medicine Taipei Medical University Taipei Taiwan Research Center of Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Division of Pulmonary Medicine Department of Internal Medicine Shuang Ho Hospital Taipei Medical University New Taipei City 235041 Taiwan Sleep Center Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Department of Biomedical Engineering Taipei Medical University Taipei Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan In-Service Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Division of Pulmonary Medicine Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei 110301 Taiwan Department of Otolaryngology Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Department of Neurology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Taipei Neuroscience Institute Taipei Medical University Taipei Taiwan Dementia Center Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Department of Neurology Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Institute of Medical Humanities Taipei Medical University Taipei Taiwan Department of Psychiatry Taipei Medical University Hospital Taipei Taiwan

Air pollutant exposure has been shown to exacerbate obstructive sleep apnea (OSA) manifestations, such as increased respiratory episodes and intermittent oxygen desaturation. However, most studies have focused on episode frequency rather than duration. This study investigated the associations between air pollution and OSA manifestations using both frequency-based indices and the duration-based marker lung-to-finger circulation time (LFCT). Polysomnography data from 820 individuals with suspected OSA (mild-to-moderate: n=224; severe: n=596) were analyzed. Sleep disorder indices included the apnea–hypopnea index (AHI), oxygen desaturation index (ODI), arousal index, and mean LFCT. A novel composite metric, respiratory event response time area (RERTA), defined as the square root of the product of LFCT and AHI, was introduced to evaluate the interplay between frequency and duration. Air pollutant exposure was estimated using data from monitoring stations within 4 km of the registered residences of the study cohort. Multivariable Poisson regression models were employed to assess short-term (1-month) and long-term (1-year) exposure effects. Short-term PM 2.5 and NO 2 exposure were significantly associated with increased AHI, ODI, arousal index, and reduced LFCT. Long-term exposure to PM 2.5 , PM 10 , and NO 2 was associated with increased AHI and ODI, while PM 2.5 and PM 10 were linked to reduced LFCT. Both short- and long-term O 3 exposure were associated with increased AHI and ODI. PM 2.5 and O 3 (short-term) were positively associated with RERTA. These findings suggest that air pollution exacerbates OSA by increasing episode frequency and accelerating compensatory responses, thereby elevating cardiopulmonary burden as reflected by RERTA.

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Online Recurrent Extreme Learning Machine and its Application to Time-series Prediction

Online Recurrent Extreme Learning Machine and its Applicatio...

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International Joint Conference on Neural Networks

作者： Jin-Man Park Jong-Hwan Kim Robotics Program KAIST School of Electrical Engineering KAIST

ISBN: (纸本)9781509061839

Online sequential extreme learning machine (OS-ELM) is an online learning algorithm training single-hidden layer feedforward neural networks (SLFNs), which can learn data one-by-one or chunk-by-chunk with fixed or varying data size. Due to its characteristics of online sequential learning, OS-ELM is popularly used to solve time-series prediction problem, such as stock forecast, weather forecast, passenger count forecast, etc. OS-ELM, however, has two fatal drawbacks: Its input weights cannot be adjusted and it cannot be applied to learn recurrent neural network (RNN). Therefore we propose a modified version of OS-ELM, called online recurrent extreme learning machine (OR-ELM), which is able to adjust input weights and can be applied to learn RNN, by applying ELM-auto-encoder and a normalization method called layer normalization (LN). Proposed method is used to solve a time-series prediction problem on New-York City passenger count dataset, and the results show that R-ELM outperforms OS-ELM and other online-sequential learning algorithms such as hierarchical temporal memory (HTM) and online long short-term memory (online LSTM).

关键词： Online sequential extreme learning machine (OS-ELM) Online recurrent extreme learning machine (OR-ELM) Online learning Time-series prediction Auto-encoding e-learning recurrent neural nets Learning machines normalization method Chunking data size learning algorithms

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Inverse reinforce learning with nonparametric behavior clustering

arXiv

引用

arXiv 2017年

作者： Rajasekaran, Siddharthan Zhang, Jinwei Fu, Jie Department of Electrical and Computer Engineering Robotics Engineering Program Worcester Polytechnic Institute

Inverse Reinforcement Learning (IRL) is the task of learning a single reward function given a Markov Decision Process (MDP) without defining the reward function, and a set of demonstrations generated by humans/experts. However, in practice, it may be unreasonable to assume that human behaviors can be explained by one reward function since they may be inherently inconsistent. Also, demonstrations may be collected from various users and aggregated to infer and predict users behaviors. In this paper, we introduce the Non-parametric Behavior Clustering IRL algorithm to simultaneously cluster demonstrations and learn multiple reward functions from demonstrations that may be generated from more than one behaviors. Our method is iterative: It alternates between clustering demonstrations into different behavior clusters and inverse learning the reward functions until convergence. It is built upon the Expectation-Maximization formulation and non-parametric clustering in the IRL setting. Further, to improve the computation efficiency, we remove the need of completely solving multiple IRL problems for multiple clusters during the iteration steps and introduce a resampling technique to avoid generating too many unlikely clusters. We demonstrate the convergence and efficiency of the proposed method through learning multiple driver behaviors from demonstrations generated from a grid-world environment and continuous trajectories collected from autonomous robot cars using the Gazebo robot simulator. Copyright © 2017, The Authors. All rights reserved.

关键词： Demonstrations

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Continuous Wrist Joint Control using Muscle Deformation Measured on Forearm Skin

Continuous Wrist Joint Control using Muscle Deformation Meas...

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IEEE International Conference on robotics and Automation

作者： Akira Kato Masato Hirabayashi Yuya Matsumoto Yasutaka Nakashima Yo Kobayashi Masakatsu G. Fujie Shigeki Sugano Graduate Program for Embodiment Informatics Waseda University Tokyo Japan Graduate Schools of Creative Science and Engineering Waseda University Tokyo Japan Faculty of Engineering Kyushu University Fukuoka Japan JST PRESTO Future Robotics Organization Waseda Universit Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

Continuous, easy-to-implement, accurate inference of intended joint angles is important for effectively controlling powered prosthetic devices that can improve the lives and capabilities of upper-limb amputees. Estimation of intended joint angles is difficult because conventional biosignals are not directly related to the intended angle motion. In previous work, we began to address this issue by confirming that both transradial amputees and intact subjects, the measured deformation of the muscle bulge on the skin surface change according to the intended wrist joint angle. This paper presents a continuous prosthesis wrist joint control method using this deformation signal. We here verify the effectiveness of the distribution of the muscle bulge for accurate and stable wrist joint angle control in real time. The wrist joint angles were calculated in real time from a muscle viscoelastic model using the previously determined algorithm. We compared the error between measured and estimated angles with a conventional method, the Voigt model, and the KelvinVoigt model. Experimental results obtained for three intact people over three trials of wrist movement tasks gave the accuracy and stability of 7.96±6.16° when using the Voigt model;this is a similar performance compared to related work using a surface electromyogram. A method for continuously controlling the wrist joint angle for a prosthesis using the distribution of the muscle bulge was thus successfully established.

关键词： Wrist Muscles Strain Skin Prosthetic hand Mathematical model Estimation

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Sampling-based approximate optimal temporal logic planning

Sampling-based approximate optimal temporal logic planning

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IEEE International Conference on robotics and Automation (ICRA)

作者： Lening Li Jie Fu Robotics Engineering Program Worcester Polytechnic Institute Worcester MA US

In this paper, we propose a sampling-based policy iteration for optimal planning under temporal logic constraints. The method integrates approximate optimal control, importance sampling, and formal methods. For a subclass of linear temporal logic, the planning problem is transformed to an optimal control problem for a hybrid system where discrete transitions are triggered by linear time events in temporal logic. Instead of solving the Hamilton-Jacobi-Bellman equation, we use policy function approximation to reduce the problem into a search of an optimal weight vector that parametrizes the near-optimal policy for given bases. Then, we incorporate Model Reference Adaptive Search - an importance sampling-based optimization algorithm to perform a sample-efficient search within the parameter space of policy function approximations. Facing the discontinuity in cost function introduced by temporal logic constraints and system dynamics, we introduce (1) a rank function in formal logic specifications to enable sample-efficient search; (2) specification-guided basis selection. Under mild technical assumptions, the proposed algorithm converges, with probability one, to a global approximate optimal policy that ensures the satisfaction of temporal logic constraints. The correctness and efficiency of the method are demonstrated through numerical experiments including temporal logic planning for a linear system and a nonlinear mobile robot.

关键词： Planning Approximation algorithms Trajectory Function approximation Nonlinear systems Optimal control Automata

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Automatic color correction for 3D reconstruction of underwater scenes

Automatic color correction for 3D reconstruction of underwat...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Skinner, Katherine A. Iscar, Eduardo Johnson-Roberson, Matthew Robotics Program University of Michigan Ann ArborMI48109 United States Department of Naval Architecture and Marine Engineering University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9781509046331

Mapping of underwater environments is a critical task for a range of activities from monitoring coral reef habitats to surveying submerged archaeological sites. While recent advances in methods for terrestrial mapping can achieve dense 3D reconstructions of scenes in real-time, there remains the challenge of transferring these methods to the underwater domain due to characteristic effects on propagation of light through the water column that violate the brightness constancy constraint used in terrestrial techniques. Current state-of-the-art methods for underwater 3D reconstruction exploit a physical model of light propagation underwater to account for such range-dependent effects as scattering and attenuation;however, these methods necessitate careful calibration of attenuation coefficients required by the physical model, or rely on rough estimates of these coefficients from prior lab experiments. The main contribution of this paper is to develop a novel method to achieve simultaneous estimation of attenuation coefficients for color correction during structure recovery of an underwater scene by integrating this estimation directly into the bundle adjustment step, which performs non-linear optimization. To validate the proposed method, an artificial scene is submerged in a pure water tank and surveyed with a stereo camera platform to simulate an underwater robotic survey in a controlled environment. The target structure is imaged in air with an RGB-D sensor to provide ground truth structure and color, and a color calibration board is place in the scene for further reference. Results show that the proposed method can automatically estimate a water-column aware model for color correction of underwater images simultaneously to 3D reconstruction of the submerged scene. © 2017 IEEE.

关键词： Mapping

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Scalar field reconstruction based on the Gaussian process and adaptive sampling

Scalar field reconstruction based on the Gaussian process an...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Porsteinn B. Jónsson Jeonghyeon Wang Jinwhan Kim Mobile Robotics and Intelligence (MORIN) Laboratory Departrnent of Mechanical Engineering KAIST Daeieon Korea Robotics Program KAIST Daejeon Korea

This paper proposes a method of reconstructing a scalar field by adaptively choosing sampling locations and using the measurements obtained from those locations to reconstruct an estimate of the underlying field using Gaussian process regression. Spreading sampling points evenly over the field may not always be effective if the field is not uniformly distributed and the maximum number of measurements is limited. Taking more measurements in regions of large changes in the field than in regions of small changes can give a better estimate than spreading the same number of measurements evenly over the space. The proposed algorithm was tested on a synthetic scalar field and compared to two popular methods of determining sensor placement based on entropy and mutual information from information theory.

关键词： Entropy Mutual information Gaussian processes Training Covariance matrices Training data Simulated annealing

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Planning and executing optimal non-entangling paths for tethered underwater vehicles

Planning and executing optimal non-entangling paths for teth...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Seth McCammon Geoffrey A. Hollinger Robotics Program of the School of Mechanical Industrial and Manufacturing Engineering Oregon State University Corvallis OR

In this paper, we present a method to improve the navigation of tethered underwater vehicles by computing optimal paths that prevent their tethers from becoming entangled in obstacles. To accomplish this, we define the Non-Entangling Travelling Salesperson Problem (NE-TSP) as an extension of the Travelling Salesperson Problem with a non-entangling constraint. We compute the optimal solution to the NE-TSP by constructing a Mixed Integer programming model, leveraging homotopy augmented graphs to plan an optimal trajectory through a set of inspection points, while maintaining a non-entangling guarantee. To avoid the computational expense of computing an optimal solution to the NE-TSP, we also introduce several methods to compute near-optimal solutions. In a set of simulated trials, our method was able to plan optimal non-entangling paths through a variety of environments. These results were then validated in a set of pool and field trials using a Seabotix vLBV300 underwater vehicle. The paths generated by our method were then compared to human-generated paths.

关键词： Trajectory Planning Inspection Computational modeling Service robots Underwater vehicles

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Development of a Hover-Capable AUV System for Automated Visual Ship-Hull Inspection and Mapping

Development of a Hover-Capable AUV System for Automated Visu...

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Oceans (Conference)

作者： Seonghun Hong Dongha Chung Jinwhan Kim Robotics Program KAIST Daejeon Korea Department of Mechanical Engineering KAIST Daejeon Korea

ISBN: (纸本)9781509064298;9780692946909

Generally, underwater hull inspection have been conducted by human divers. It is an extremely dangerous task, and hence, can be a potential application for unmanned underwater vehicles. The operational safety and performance of in-water inspection can be significantly improved by introducing unmanned vehicle systems. This study addresses the development of an hover-capable autonomous underwater vehicle system and its operational algorithms for automated visual ship hull inspection with no (or minimum) human intervention. The feasibility and practical performance of the developed system and algorithms are demonstrated by conducting field experiments with a full-scale ship in a real sea environment.

关键词： Autonomous underwater vehicles Visual inspection Underwater navigation Image mosaicking

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