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IEEE International Conference on robotics and Automation (ICRA)

作者： Andreas Orthey Marc Toussaint Nikolay Jetchev LAAS Université de Toulouse UPS INSA INP ISAE Centre National de la Recherche Scientifique Toulouse France Machine Learning and Robotics Laboratory University of Stuttgart Stuttgart Germany Machine Learning and Robotics Laboratory FU Berlin Berlin Germany

ISBN: (纸本)9781467356404

Solving complex robot manipulation tasks requires to combine motion generation on the geometric level with planning on a symbolic level. On both levels robotics research has developed a variety of mature methodologies, including geometric motion planning and motion primitive learning on the motor level as well as logic reasoning and relational Reinforcement learning methods on the symbolic level. However, their robust integration remains a great challenge. In this paper we approach one aspect of this integration by optimizing the motion primitives on the geometric level to be as consistent as possible with their symbolic predictions. The so optimized motion primitives increase the probability of a “successful” motion-meaning that the symbolic prediction was indeed achieved. Conversely, using these optimized motion primitives to collect new data about the effects of actions the learnt symbolic rules becomes more predictive and deterministic.

关键词： Trajectory Noise Robots Grasping Cost function Linear programming

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Clustering data and imprecise concepts

Clustering data and imprecise concepts

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Weifeng Zhang Zengchang Qin Intelligent Computing and Machine Learning Laboratory School of Automation Science and Electrical Engineering Beihang University China Robotics Institute CMU USA Intelligent Computing and Machine Learning Laboratory School of ASEE Beihang University China

Cluster analysis is the assignment of grouping a set of observations into clusters so that observations in the same cluster are similar in some sense. One of the key features for clustering is how to define a sensible similarity measure. However, classical clustering algorithms have no ability to cluster data instances and imprecise concepts using traditional distance measures. In this paper, we proposed a (dis)similarity measure based on a new knowledge representation framework called label semantics. Based on this new measure, we can automatically cluster data instance and descriptive concepts represented by logical expressions of linguistic labels. Experimental results on a toy problem in image classification demonstrate the effectiveness of the new proposed clustering algorithm. Since the new proposed measure can be extended to measuring distance between any two granularities, the new clustering algorithms can also be extended to clustering data instance and imprecise concepts represented by other granularities.

关键词： Pragmatics Clustering algorithms Semantics Image color analysis Silicon Algorithm design and analysis Humans

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Sparse Gaussian process regression for compliant, real-time robot control

Sparse Gaussian process regression for compliant, real-time ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jens Schreiter Peter Englert Duy Nguyen-Tuong Marc Toussaint Robert Bosch GmbH Department for Cognitive Systems Stuttgart Germany University of Stuttgart Machine Learning and Robotics Laboratory Stuttgart Germany

ISBN: (纸本)9781479969241

Sparse Gaussian process (GP) models provide an efficient way to perform regression on large data sets. The key idea is to select a representative subset of the available training data, which induces the sparse GP model approximation. In the past, a variety of selection criteria for GP approximation have been proposed, but they either lack accuracy or suffer from high computational costs. In this paper, we introduce a novel and straightforward criterion for successive selection of training points used for GP model approximation. The proposed algorithm allows a fast and efficient selection of training points, while being competitive in learning performance. As evaluation, we employ our approach in learning inverse dynamics models for robot control using very large data sets (e.g. 500.000 samples). It is demonstrated in experiments that our approximated GP model is sufficiently fast for real-time prediction in robot control. Comparisons with other state-of-the-art approximation techniques show that our proposed approach is significantly faster, while being competitive to generalization accuracy.

关键词： Approximation methods Training Ground penetrating radar Computational modeling Data models Robots Accuracy

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Image scene categorization using multi-bag-of-features

Image scene categorization using multi-bag-of-features

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Weifeng Zhang Zengchang Qin Tao Wan Intelligent Computing and Machine Learning Laboratory (ICMLL) School of ASEE Beihang University China Robotics Institute Carnegie Mellon University USA

Image scene classification, the classification of images into semantic categories, e.g. city, urban, sea, etc, has recently become a vigorous research focus in computer vision for its broad application prospect. In this paper, we propose a novel approach to understand image semantic scene based on multi-bag-of-features. We aim to design an efficient but simple scene classification algorithm via fusing multiple low-level image features. Experimental results demonstrate that the proposed approach offers an effective way to classify the complex image scenes by using a multi-bag-of-features model.

关键词： Support vector machines Books Quantization Image segmentation Wide area networks

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Synchronization in interacting networks of Hodgkin-Huxley neurons

Synchronization in interacting networks of Hodgkin-Huxley ne...

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Saratov Fall Meeting 2021: Computational Biophysics and Nanobiophotonics

作者： Andreev, Andrey V. Maksimenko, Vladimir A. Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya Str. 1 Innopolis 420500 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University A. Nevskogo ul. 14 Kaliningrad236016 Russia

ISBN: (数字)9781510653702

ISBN: (纸本)9781510653696

We consider a network of networks consisting of small input neural network and four small-world subnetworks Hodgkin-Huxley neurons. Input network receives an external signal and transfers it to subnetworks via excita-tory couplings while the subnets interact with each other via inhibitory couplings. We show that the subnets are divided into 2 clusters under the influence of an inhibitory couplings between them. The synchronization indexes of subnetworks periodically change in time. We found that SIs can oscillate either in-phase or anti-phase depending on the couplings between subnetworks. © 2022 SPIE.

关键词： Complex networks

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Exploration via planning for information about the optimal trajectory 22

Exploration via planning for information about the optimal t...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Viraj Mehta Ian Char Joseph Abbate Rory Conlin Mark D. Boyer Stefano Ermon Jeff Schneider Willie Neiswanger Robotics Institute Machine Learning Department Carnegie Mellon University Princeton University Princeton Plasma Physics Laboratory Computer Science Department Stanford University

ISBN: (纸本)9781713871088

Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying RL to real-world control tasks, e.g. in the sciences or robotics, where executing a policy in the environment is costly. In popular RL algorithms, agents typically explore either by adding stochasticity to a reward-maximizing policy or by attempting to gather maximal information about environment dynamics without taking the given task into account. In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account. The key insight to our approach is to plan an action sequence that maximizes the expected information gain about the optimal trajectory for the task at hand. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines and 200x fewer samples than model free methods on a diverse set of low-to-medium dimensional control tasks in both the open-loop and closed-loop control settings.

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Analysis of behavioral characteristics during prolonged cognitive load 5

Analysis of behavioral characteristics during prolonged cogn...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Kuc, Alexander Grubov, Vadim Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia Center of New Cardiological Informational Technologies Scientific Research Institute of Cardiology Saratov State Medical University Saratov Russia

ISBN: (纸本)9781665442824

In the present work we studied human behavioral characteristics during prolonged cognitive activity. For this purpose we conducted experiment with interpretation of consistently presented ambiguous visual stimuli with different levels of ambiguity. As behavioral characteristic we chose the correctness of ambiguous stimuli interpretation and we showed, that number of errors changes significantly through the experiment. © 2021 IEEE

关键词： Behavioral research

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Neural dynamical systems: Balancing structure and flexibility in physical prediction

arXiv

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arXiv 2020年

作者： Mehta, Viraj Char, Ian Neiswanger, Willie Chung, Youngseog Nelson, Andrew Oakleigh Boyer, Mark D. Kolemen, Egemen Schneider, Jeff Robotics Institute Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Princeton Plasma Physics Laboratory United States

We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models in various gray-box settings which incorporates prior knowledge in the form of systems of ordinary differential equations. NDS uses neural networks to estimate free parameters of the system, predicts residual terms, and numerically integrates over time to predict future states. A key insight is that many real dynamical systems of interest are hard to model because the dynamics may vary across rollouts. We mitigate this problem by taking a trajectory of prior states as the input to NDS and train it to dynamically estimate system parameters using the preceding trajectory. We find that NDS learns dynamics with higher accuracy and fewer samples than a variety of deep learning methods that do not incorporate the prior knowledge and methods from the system identification literature which do. We demonstrate these advantages first on synthetic dynamical systems and then on real data captured from deuterium shots from a nuclear fusion reactor. Finally, we demonstrate that these benefits can be utilized for control in small-scale experiments. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamical systems

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Planning Ergonomic Sequences of Actions in Human-Robot Interaction

Planning Ergonomic Sequences of Actions in Human-Robot Inter...

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IEEE International Conference on robotics and Automation

作者： Baptiste Busch Marc Toussaint Manuel Lopes FLOWERS Team an Inria BSO ENSTA Paritech joint laboratory France Machine Learning and Robotics Lab University of Stuttgart Germany INESC-ID Universide de Lisboa Portugal

In this paper, we define the problem of human-robot collaboration as a combined task and motion planning problem which is extended to the multi-agent case (human and robot). Our proposed approach allows us to explicitly take into account ergonomic cost, synchrony and concurrency of behavior in an optimization formulation. We show simulated results as well as an experiment with a real robot combined with a user study. Results show that optimizing over a sequence of actions leads to more ergonomic situations.

关键词： Ergonomics Task analysis Robot kinematics Planning Cost function

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Object Segmentation based on Anti-disturb Network in Ecological Surveillance

Object Segmentation based on Anti-disturb Network in Ecologi...

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第33届中国控制与决策会议

作者： Wan Dai Hongpeng Wang Chongshan Fan Yunwei Xin College of Computer Science Nankai University Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Nankai University Software College Nankai University Research Laboratory of Machine Learning and Pervasive Computing Nankai University

Visual tele-observation is an effective way for ecological *** from pedestrian or rigid body detection,wildlife recognition in natural scenes face more complex problems,such as the similarity of background and foreground,wild background clutter,deformation,and other factors,which will lead to a reduction in recognition *** this paper,We propose a novel segmentation network that is named T-S network to segment background and *** network mainly consists of two parts:one is a tracking *** the context similarity of images in the data set,the purpose of tracking is to ensure that the position of the object is accurately known when the same species in the images are synthesized into a video with interval frames;the other is the saliency network,which is the basis of the most likely position of the object obtained from the tracking ***,the saliency network is used to complete the object *** verify the effectiveness of the network and consider the size of the data set,a lightweight CNN is used to identify the *** show that the accuracy of task recognition is improved by 1.89% by using the segmentation method proposed in this paper.

关键词： segmentation recognition tracking network saliency network

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