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International Conference on Control, Automation and Systems

作者： Jinkwang Kim Geonhoo Kim Taewoo Han Seungwon Heo Yongjae Kim Hyun Myung Department of the Robotics Program KAIST Daejeon Korea Department of Electronics Engineering Korea University of Technology and Education(Koreatech) Cheonan Korea Department of Civil and Environmental Engineering KAIST Daejeon Korea

ISBN: (纸本)9781538670804;9788993215168

This paper reports the design, construction, and control of a mobile robot that can be transformed from the four-wheel mobile robot into two-wheel self-balancing robot and vice versa. The hardware of the robot utilizes the mechanism of the three-link manipulator. This robot is composed of three components;the body part, the middle link, and the top part. The system architecture comprises a pair of DC motor controllers to move the wheel, two servo motors to move the middle link and the top part, and an Arduino microcontroller board, etc. When the robot is transformed to the two-wheel self-balancing robot, the COM (Center of Mass) equation, and the IMU (Inertial Measurement Unit) sensor are employed for attitude determination. The method of the control is based on a proportional-integral-differential (PID) control. The results show the possibility of performing both functions of the four-wheel mobile robot and two-wheel self-balancing robot.

关键词： Three-link manipulator Two-wheel self-balancing robot Center of mass

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WE-184. From “irritative zone” to “irritative network”: a novel interictal estimate of the epileptogenic zone using invasive EEG

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Clinical Neurophysiology 2022年 141卷 S86-S86页

作者： Pesce, Matthew Billardello, Roberto Pace, Lavinia Madsen, Joseph Pearl, Phillip Papadelis, Christos Grant, Ellen Tamilia, Eleonora Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children's Hospital Boston USA Research Unit of Advanced Robotics and Human-Centered Technologies Department of Engineering University Bio-Medico Campus of Rome Rome Italy Epilepsy Surgery Program Department of Neurosurgery Boston Children’s Hospital Boston USA Harvard Medical School Boston USA Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children’s Hospital Boston USA Jane and John Justin Neurosciences Center Cook Children’s Health Care System Fort Worth USA Department of Bioengineering University of Texas at Arlington Arlington USA Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital Boston USA

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Privacy-sensitive robotics 12

Privacy-sensitive robotics

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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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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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CWave: High-performance single-source any-angle path planning on a grid

CWave: High-performance single-source any-angle path plannin...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Sinyukov, Dmitry A. Padir, Taskin Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9781509046331

Path planning on a 2D-grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid. Single-source path planning is a modified problem which asks to find distances from a given point to all other points on the map. A high-performance algorithm for single-source any-angle path planning on a grid that we named CWave is proposed in this work. 'Any-angle' attribute of a path planning algorithm implies that such algorithm can find paths which may include any angle segments, as opposed to standard A © 2017 IEEE.

关键词： Motion planning

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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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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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Inverse reinforce learning with nonparametric behavior clustering

arXiv

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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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Framing Effects on Privacy Concerns About a Home Telepresence Robot

Framing Effects on Privacy Concerns About a Home Telepresenc...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Matthew Hueben Frank J. Bernieri Cindy M. Grimm William D. Smart Robotics Program School of Psychological Science Oregon State University Corvallis OR United States

ISBN: (纸本)9781450343367

Privacy-sensitive robotics is an emerging area of HRI research. Judgments about privacy would seem to be context-dependent, but none of the promising work on contextual “frames” has focused on privacy concerns. This work studies the impact of contextual “frames” on local users' privacy judgments in a home telepresence setting. Our methodology consists of using an online questionnaire to collect responses to animated videos of a telepresence robot after framing people with an introductory paragraph. The results of four studies indicate a large effect of manipulating the robot operator's identity between a stranger and a close confidante. It also appears that this framing effect persists throughout several videos. These findings serve to caution HRI researchers that a change in frame could cause their results to fail to replicate or generalize. We also recommend that robots be designed to encourage or discourage certain frames.

关键词： Privacy Videos Telepresence Robot sensing systems Psychology Animation

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