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ieee International Conference on Robotics and automation (ICRA)

作者： Dupeyroux, Julien Hagenaars, Jesse J. Paredes-Valles, Federico de Croon, Guido C. H. E. Delft Univ Technol Micro Air Vehicle Lab Fac Aerosp Engn Delft Netherlands

ISBN: (纸本)9781728190778

Neuromorphic processors like Loihi offer a promising alternative to conventional computing modules for endowing constrained systems like micro air vehicles (MAVs) with robust, efficient and autonomous skills such as take-off and landing, obstacle avoidance, and pursuit. However, a major challenge for using such processors on robotic platforms is the reality gap between simulation and the real world. In this study, we present for the very first time a fully embedded application of the Loihi neuromorphic chip prototype in a flying robot. A spiking neural network (SNN) was evolved to compute the thrust command based on the divergence of the ventral optic flow field to perform autonomous landing. Evolution was performed in a Python-based simulator using the PySNN library. The resulting network architecture consists of only 35 neurons distributed among 3 layers. Quantitative analysis between simulation and Loihi reveals a root-meansquare error of the thrust setpoint as low as 0.005 g, along with a 99.8% matching of the spike sequences in the hidden layer, and 99.7% in the output layer. The proposed approach successfully bridges the reality gap, offering important insights for future neuromorphic applications in robotics. Supplementary material is available at https://***/loihi/.

关键词： Program processors Neuromorphics Statistical analysis computational modeling Neurons Prototypes Unmanned aerial vehicles

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Reinforcement Learning with Temporal Logic Specifications for Regression Testing NPCs in Video Games

Reinforcement Learning with Temporal Logic Specifications fo...

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ieee symposium on computational intelligence and Games, CIG

作者： Pablo Gutiérrez-Sánchez Marco A. Gómez-Martín Pedro A. González-Calero Pedro P. Gómez-Martín Software Engineering and Artificial Intelligence Department Complutense University of Madrid Madrid Spain

Reinforcement learning (RL) is a promising strategy for the development of autonomous agents in various control and optimization contexts, including the generation of autonomous players in video games. However, designing these agents, and in particular their reward functions to perform sequential decision-making, can be challenging for most users and often require tedious trial-and-error processes until a satisfactory result is obtained. Consequently, these strategies are generally beyond reach for designers and quality control teams, who could potentially make use of them to generate automatic testing agents. This paper presents the application of reinforcement learning and behavioral descriptions given through a formal temporal logic task specification language (TLTL) for the design of NPCs that can be employed as surrogates for the player in such contexts. We argue that these techniques enable designers to naturally specify the way in which they would expect the final player to interact with a level and then generate a test that automatically verifies whether this strategy continues to be feasible throughout the development of the game. We include a series of experiments conducted on a custom 3D test environment developed in Unity3D that show that the proposed methodology provides a simple mechanism for training NPCs in settings that are commonly encountered in modern video games.

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Learning a Centroidal Motion Planner for Legged Locomotion

Learning a Centroidal Motion Planner for Legged Locomotion

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ieee International Conference on Robotics and automation (ICRA)

作者： Viereck, Julian Righetti, Ludovic NYU Tandon Sch Engn New York NY 10012 USA Max Planck Inst Intelligent Syst Tubingen Tubingen Germany

ISBN: (纸本)9781728190778

Whole-body optimizers have been successful at automatically computing complex dynamic locomotion behaviors. However they are often limited to offline planning as they are computationally too expensive to replan with a high frequency. Simpler models are then typically used for online replanning. In this paper we present a method to generate whole body movements in real-time for locomotion tasks. Our approach consists in learning a centroidal neural network that predicts the desired centroidal motion given the current state of the robot and a desired contact plan. The network is trained using an existing whole body motion optimizer. Our approach enables to learn with few training samples dynamic motions that can be used in a complete whole-body control framework at high frequency, which is usually not attainable with typical full-body optimizers. We demonstrate our method to generate a rich set of walking and jumping motions on a real quadruped robot.

关键词： Legged locomotion Training Conferences computational modeling Neural networks Dynamics Real-time systems

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Robust localization for planar moving robot in changing environment: A perspective on density of correspondence and depth

Robust localization for planar moving robot in changing envi...

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ieee International Conference on Robotics and automation (ICRA)

作者： Jiao, Yanmei Liu, Lilu Fu, Bo Ding, Xiaqing Wang, Minhang Wang, Yue Xiong, Rong Zhejiang Univ State Key Lab Ind Control & Technol Hangzhou Peoples R China Huawei Inc Co Applicat Innovate Lab Shenzhen Peoples R China

ISBN: (纸本)9781728190778

Visual localization for planar moving robot is important to various indoor service robotic applications. To handle the textureless areas and frequent human activities in indoor environments, a novel robust visual localization algorithm which leverages dense correspondence and sparse depth for planar moving robot is proposed. The key component is a minimal solution which computes the absolute camera pose with one 3D-2D correspondence and one 2D-2D correspondence. The advantages are obvious in two aspects. First, the robustness is enhanced as the sample set for pose estimation is maximal by utilizing all correspondences with or without depth. Second, no extra effort for dense map construction is required to exploit dense correspondences for handling textureless and repetitive texture scenes. That is meaningful as building a dense map is computational expensive especially in large scale. Moreover, a probabilistic analysis among different solutions is presented and an automatic solution selection mechanism is designed to maximize the success rate by selecting appropriate solutions in different environmental characteristics. Finally, a complete visual localization pipeline considering situations from the perspective of correspondence and depth density is summarized and validated on both simulation and public real-world indoor localization dataset.

关键词： Location awareness Visualization Pose estimation Buildings Robot vision systems Pipelines Probabilistic logic

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Co-evolving morphology and control of soft robots using a single genome

Co-evolving morphology and control of soft robots using a si...

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ieee symposium Series on computational intelligence (SSCI)

作者： Fabio Tanaka Claus Aranha Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Department of Computer Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781665487696

When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the “brain” and the “body” of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects “brain” and the “body” at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.

关键词： Robot kinematics Sociology Morphology Genomics Process control Soft robotics Bioinformatics

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Inverse Kinematics Analysis of Cassie Robot using Radial Basis Function Networks

Inverse Kinematics Analysis of Cassie Robot using Radial Bas...

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2021 International symposium of Asian control Association on Intelligent Robotics and Industrial automation, IRIA 2021

作者： Mitra, Mukund Raj, Suman Kolathaya, Shishir Indian Institute of Science Centre for Product Design and Manufacturing Bangalore India Indian Institute of Science Department of Computational and Data Sciences Bangalore India Indian Institute of Science Department of Computer Science and Automation Bangalore India

ISBN: (纸本)9781665433235

Inverse Kinematics of bipedal humanoid robots remains a challenging problem in the domain of robotics and computation, due to high order non-linearity and computation involved in Inverse Kinematics solutions. Also, there are many constraints involved with the various joint parameters which makes their analysis even more complex. Through this paper, we attempt to solve the Inverse Kinematics problem of a bipedal humanoid robot, Cassie, using Radial Basis Function (RBF) Networks. Our method can also be applied to other higher degrees of freedom serial manipulators. Our simulation analyses the results based on size of datasets, data distribution and network parameters. We have considered datasets of size ∼300k and ∼1 million, single and multiple hidden layers, equal and random data distribution, different number of neurons in layers and different training functions. We achieve our target of limiting the Mean Squared Error (MSE) calculated using the trained model below 0.1° for each joint angle, which is under acceptable limits for practical implementation. The configurations obtained from the RBF network are simulated and compared with the original input configuration. This is compared visually in MATLAB and the resulting pose of end-effector are similar for both the cases, complementing the performance that we get for the networks. © 2021 ieee.

关键词： Radial basis function networks

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Parallel medical imaging: An ACP-based approach for intelligent medical image recognition with small samples 1

Parallel medical imaging: An ACP-based approach for intellig...

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1st ieee International Conference on Digital Twins and Parallel intelligence, DTPI 2021

作者： Shen, Tianyu Gou, Chao Wang, Jiangong Huang, Jun He, Yonglan Xue, Huadan Jin, Zhengyu Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Guangzhou China Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Department of Radiology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665433372

The deep learning methods trained with large-scale manually annotated datasets have led to significant breakthroughs in medical image community. However, obtaining such datasets remains a challenging work in medical domain. In this paper, we propose an ACP-based approach named parallel medical imaging (PMI) for analyzing and addressing the small sample problem of medical image recognition. Firstly, we present the basic framework and key techniques of PMI, in which the Artificial imaging systems are constructed to effectively augment the 'medical small sample', the computational experiments are carried out for designing and evaluating the visual models trained with small samples, and the Parallel execution is conducted to achieve closed-loop interaction and optimization in the domain-knowledge guidance. Furthermore, we illustrate a preliminary implementation and application of PMI approach through the case study of mammogram analysis. © 2021 ieee.

关键词： Medical imaging

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Ontological Approach to Creating Viable Intelligent Systems

Ontological Approach to Creating Viable Intelligent Systems

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2021 International symposium on Knowledge, Ontology, and Theory, KNOTH 2021

作者： Gribova, Valeriya Shalfeeva, Elena Institute of Automation and Control Processes Far Eastern Branch of Russian Academy of Sciences Vladivostok Russia

ISBN: (纸本)9781665467193

The modern challenge to artificial intelligence systems is to reduce the complexity of their development and maintenance during the life cycle, as well as the explanation of the solutions obtained. The paper shows that the ontological infrastructure that provides the creation and control of all components of systems with knowledge bases, can successfully ensure the fulfillment of these requirements. The paper describes approaches to solving the problem of controllability of this class of systems and, above all, the component most susceptible to changes - the knowledge base, describes an approach to the formation of ontological solvers from reusable components with the explanations generation. The proposed technology and its instrumental support are implemented on the IACPaaS platform, and a number of application systems for various domains have been developed using it. © 2021 ieee.

关键词： Intelligent systems

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A Modular Supervisory control Scheme for the Safety of an Automated Manufacturing System

A Modular Supervisory Control Scheme for the Safety of an Au...

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Robotics, automation and Artificial intelligence (RAAI), ieee International Conference on

作者： Nikolaos D. Kouvakas Fotis N. Koumboulis Dimitrios G. Fragkoulis Konstantinos Markou Department of Digital Industry Technologies School of Science National and Kapodistrian University of Athens Euripus Campus Greece Core Department National and Kapodistrian University of Athens Euripus Campus Greece

The analytic finite deterministic automata models of the subsystems of an automated manufacturing system in circular mode are presented. The total model of the manufacturing system is determined. The safety specifications of the system are formulated in the form of rules. The rules are translated to appropriate desired regular languages. The languages are realized in analytic forms of finite deterministic supervisor automata. A modular supervisory control scheme based on the supervisor automata, is proposed. The satisfactory performance of the controlled automaton is proven, through the computation of the marked language of the controlled automaton and the proof the nonblocking property of the controlled automaton as well as the proof of the controllability of the languages, realized by the supervisor automata. The complexity of the proposed supervisor scheme is computed.

关键词： computational modeling Automata Process control Computer architecture Supervisory control controllability Safety

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Garbage Disposal of Complex Background Based on Deep Learning With Limited Hardware Resources

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ieee SENSORS JOURNAL 2021年第18期21卷 21050-21058页

作者： Feng, Jiewen Tang, Xiaoyu Jiang, Xingjian Chen, Qunyuan South China Normal Univ Minist Educ Key Lab Brain Cognit & Educ Sci Guangzhou 510006 Peoples R China South China Normal Univ Natl Demonstrat Ctr Expt Phys Educ Sch Phys & Telecommun Engn Guangzhou 510006 Peoples R China South China Normal Univ Guangdong Prov Key Lab Quantum Engn & Quantum Mat Guangdong Prov Engn Technol Res Ctr Optoelect Ins Sch Phys & Telecommun Engn Guangzhou 510006 Peoples R China Univ Hong Kong Dept Comp Engn Fac Engn Hong Kong Peoples R China

With the increasingly severe problems of garbage contamination, automatic garbage identification has significant value in actual application. However, the real implementation is still confronted with the limitations of equipment performance and the need for more computer resources. To effectively tackle the challenges, this paper proposes a model that uses the MobileNet as a base network to improve the performance of Mask R-CNN, which could be applied to the low-cost embedded devices. For a numerical evaluation on real images, we construct a garbage image data set containing multiple morphological types based on the complex background. Specifically, when pre-processing the data set, we use the methods of data expansion and transfer learning. Consequently, high-precision detection of garbage could be achieved under the condition of a few samples. The experimental results present that the approach can achieve a detection accuracy up to 93%. Furthermore, the proposed approach is a complete system combining both hardware and software design, which dramatically promotes the automation of production and provides a reference for the intelligent garbage sorting machines.

关键词： Standards Sensors Hardware Kernel Deep learning Sorting computational modeling Artificial intelligence deep learning embedding system garbage detection intelligent control

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