DOCSLIB.ORG

Real-Time Hebbian Learning from Autoencoder Features for Control Tasks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Real-Time Hebbian Learning from Autoencoder Features for Control Tasks Real-time Hebbian Learning from Autoencoder Features for Control Tasks Justin K. Pugh1, Andrea Soltoggio2, and Kenneth O. Stanley1 1Dept. of EECS (Computer Science Division), University of Central Florida, Orlando, FL 32816 USA 2Computer Science Department, Loughborough University, Loughborough LE11 3TU, UK [email protected], [email protected], [email protected] Downloaded from http://direct.mit.edu/isal/proceedings-pdf/alife2014/26/202/1901881/978-0-262-32621-6-ch034.pdf by guest on 25 September 2021 Abstract (Floreano and Urzelai, 2000), neuromodulation (Soltoggio et al., 2008), the evolution of memory (Risi et al., 2011), and Neural plasticity and in particular Hebbian learning play an reward-mediated learning (Soltoggio and Stanley, 2012). important role in many research areas related to artficial life. By allowing artificial neural networks (ANNs) to adjust their However, while Hebbian rules naturally facilitate learn- weights in real time, Hebbian ANNs can adapt over their ing correlations between actions and static features of the lifetime. However, even as researchers improve and extend world, their application in particular to control tasks that re- Hebbian learning, a fundamental limitation of such systems quire learning new features in real time is more complicated. is that they learn correlations between preexisting static fea- tures and network outputs. A Hebbian ANN could in principle While some models in neural computation in fact do enable achieve significantly more if it could accumulate new features low-level feature learning by placing Hebbian neurons in over its lifetime from which to learn correlations. Interest- large topographic maps with lateral inhibition (Bednar and ingly, autoencoders, which have recently gained prominence Miikkulainen, 2003), such low-level cortical models gener- feature in deep learning, are themselves in effect a kind of ally require prohibitive computational resources to integrate accumulator that extract meaningful features from their in- puts. The insight in this paper is that if an autoencoder is into real-time control tasks or especially into evolutionary connected to a Hebbian learning layer, then the resulting Real- experiments that require evaluating numerous separate indi- time Autoencoder-Augmented Hebbian Network (RAAHN) viduals. Thus there is a need for a convenient and reliable can actually learn new features (with the autoencoder) while si- feature generator that can accumulate features from which multaneously learning control policies from those new features Hebbian neurons combined with neuromodulation (Soltoggio (with the Hebbian layer) in real time as an agent experiences its environment. In this paper, the RAAHN is shown in a simu- et al., 2008) can learn behaviors in real time. lated robot maze navigation experiment to enable a controller Interestingly, such a feature-generating system already ex- to learn the perfect navigation strategy significantly more of- ten than several Hebbian-based variant approaches that lack ists and in fact has become quite popular through the rise of the autoencoder. In the long run, this approach opens up the deep learning (Bengio et al., 2007; Hinton et al., 2006; Le intriguing possibility of real-time deep learning for control. et al., 2012; Marc’Aurelio et al., 2007): the autoencoder (Hin- ton and Zemel, 1994; Bourlard and Kamp, 1988). Autoen- coders, which can be trained through a variety of algorithms Introduction from Restricted Boltzmann Machines (RBMs) (Hinton et al., As a medium for adaptation and learning, neural plasticity has 2006) to more conventional stochastic gradient descent (Le long captivated artificial life and related fields (Baxter, 1992; et al., 2012) (e.g. similar to backpropagation; Rumelhart et al. Floreano and Urzelai, 2000; Niv et al., 2002; Soltoggio et al., 1986), aim simply to output the same pattern as they input. 2008, 2007; Soltoggio and Jones, 2009; Soltoggio and Stan- By training them to mimic their inputs, they are forced to ley, 2012; Risi and Stanley, 2010; Risi et al., 2011; Risi and learn key features in their hidden layer that efficiently en- Stanley, 2012; Stanley et al., 2003; Coleman and Blair, 2012). code such inputs. In this way, they accumulate such key Much of this body of research focuses on Hebbian-inspired features as they are exposed to more inputs. However, in rules that change the weights of connections in proportion deep learning autoencoders are usually trained for classifica- to the correlation of source and target neuron activations tion tasks through an unsupervised pre-training phase and (Hebb, 1949). The simplicity of such Hebbian-inspired rules in fact recent results have raised doubts on their necessity makes them easy and straightforward to integrate into larger for such tasks anyway (Cires¸an et al., 2012). The idea in systems and experiments, such as investigations into the evo- this paper is that in fact autoencoders can instead be put to lution of plastic neural networks (Floreano and Urzelai, 2000; good use as feature accumulators that work synergistically Soltoggio et al., 2008; Risi et al., 2011). Thus they have en- with neuromodulated Hebbian connections that learn from abled inquiry into such diverse problems as task switching the accumulating features in real time, as an autonomous ALIFE 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems agent acts in the world. The resulting structure is the Real- an initial step. If Hebbian connections can be trained from time Autoencoder-Augmented Hebbian Network (RAAHN), a a dynamically adjusting autoencoder, then perhaps one day novel algorithm for learning control policies through rewards they will learn in real time from deepening stacked autoen- and penalties in real time. coders or within complex evolved networks that incorporate In short, the key idea behind the RAAHN is that a missing autoencoders as a basic element. While much remains to be ingredient that can reinvigorate the field of Hebbian learning explored, the initial study here thereby hints at what might is the ability of an autonomous agent to learn new features be possible in the future. as it experiences the world. Those new features are then simultaneously the inputs to a neuromodulated Hebbian layer Background that learns to control the agent based on the accumulating This section reviews Hebbian learning in artificial neural features. In effect, the RAAHN realizes the philosophy that networks (ANNs) and the application of autoencoders in real-time reward-modulated learning cannot achieve its full deep learning. Downloaded from http://direct.mit.edu/isal/proceedings-pdf/alife2014/26/202/1901881/978-0-262-32621-6-ch034.pdf by guest on 25 September 2021 potential unless the agent is simultaneously and continually learning to reinterpret and re-categorize its world. Introduc- Hebbian ANNs ing the RAAHN thereby creates the opportunity to build and The plain Hebbian plasticity rule is among the simplest for study such systems concretely. training ANNs: The experiment in this paper is intended as a proof of ∆wi = ηxiy, (1) concept designed to demonstrate that it is indeed possible to where wi is the weight of the connection between two neu- learn autoencoded (and hence unsupervised) features at the rons with activation levels xi and y, and η is the learning same time as Hebbian connections are dynamically adapting rate. As an entirely local learning rule, it is appealing both based both on correlations with the improving feature set and for its simplicity and biological plausibility. For this reason, neuromodulatory reward signals. In the experiment, simu- many researchers have sought to uncover the full extent of lated robots with two kinds of sensors (one to see the walls functionality attainable by ANNs only of Hebbian rules. and the other to see a non-uniform distribution of “crumbs” As researchers have gained insight into such networks, on the floor) are guided through a maze to show them the op- they have also found ways to increase the rule’s sophisti- timal path, after which they are released to navigate on their cation by elaborating on its central theme of strengthening own. However, supervised learning does not take place in the through correlated firing (e.g. Oja 1982; Bienenstock et al. conventional sense during this guided period. Instead, both 1982). Researchers also began to evolve such ANNs in the the autoencoder and the Hebbian connections are adjusting hope of achieving more brain-like functionalities by produc- in real time without supervisory feedback to the autoencoder. ing networks that change over their lifetime (Floreano and Furthermore, to isolate the advantage of the autoencoder, two Urzelai, 2000; Niv et al., 2002; Risi and Stanley, 2010; Risi other variants are attempted – in one the Hebbian connec- et al., 2011; Stanley et al., 2003). tions learn instead from the raw inputs and in the other the One especially important ingredient for Hebbian ANNs Hebbian connections learn from a set of random features is neuromodulation, which in effect allows Hebbian connec- (e.g. somewhat like an extreme learning machine; Huang and tions to respond to rewards and penalties. Neuromodulation Wang 2011). enables the increase, decrease, or reversal of Hebbian plas- The main result is that only the full RAAHN learns the ticity according to feedback from the environment. Models optimal path through the maze in more than a trivial percent- augmented with neuromodulation have been shown
Load more

Recommended publications
  • Control in Robotics
    Control in Robotics Mark W. Spong and Masayuki Fujita Introduction The interplay between robotics and control theory has a rich history extending back over half a century. We begin this section of the report by briefly reviewing the history of this interplay, focusing on fundamentals—how control theory has enabled solutions to fundamental problems in robotics and how problems in robotics have motivated the development of new control theory. We focus primarily on the early years, as the importance of new results often takes considerable time to be fully appreciated and to have an impact on practical applications. Progress in robotics has been especially rapid in the last decade or two, and the future continues to look bright. Robotics was dominated early on by the machine tool industry. As such, the early philosophy in the design of robots was to design mechanisms to be as stiff as possible with each axis (joint) controlled independently as a single-input/single-output (SISO) linear system. Point-to-point control enabled simple tasks such as materials transfer and spot welding. Continuous-path tracking enabled more complex tasks such as arc welding and spray painting. Sensing of the external environment was limited or nonexistent. Consideration of more advanced tasks such as assembly required regulation of contact forces and moments. Higher speed operation and higher payload-to-weight ratios required an increased understanding of the complex, interconnected nonlinear dynamics of robots. This requirement motivated the development of new theoretical results in nonlinear, robust, and adaptive control, which in turn enabled more sophisticated applications. Today, robot control systems are highly advanced with integrated force and vision systems.
    [Show full text]
  • An Abstract of the Dissertation Of
    AN ABSTRACT OF THE DISSERTATION OF Austin Nicolai for the degree of Doctor of Philosophy in Robotics presented on September 11, 2019. Title: Augmented Deep Learning Techniques for Robotic State Estimation Abstract approved: Geoffrey A. Hollinger While robotic systems may have once been relegated to structured environments and automation style tasks, in recent years these boundaries have begun to erode. As robots begin to operate in largely unstructured environments, it becomes more difficult for them to effectively interpret their surroundings. As sensor technology improves, the amount of data these robots must utilize can quickly become intractable. Additional challenges include environmental noise, dynamic obstacles, and inherent sensor non- linearities. Deep learning techniques have emerged as a way to efficiently deal with these challenges. While end-to-end deep learning can be convenient, challenges such as validation and training requirements can be prohibitive to its use. In order to address these issues, we propose augmenting the power of deep learning techniques with tools such as optimization methods, physics based models, and human expertise. In this work, we present a principled framework for approaching a prob- lem that allows a user to identify the types of augmentation methods and deep learning techniques best suited to their problem. To validate our framework, we consider three different domains: LIDAR based odometry estimation, hybrid soft robotic control, and sonar based underwater mapping. First, we investigate LIDAR based odometry estimation which can be characterized with both high data precision and availability; ideal for augmenting with optimization methods. We propose using denoising autoencoders (DAEs) to address the challenges presented by modern LIDARs.
    [Show full text]
  • Curriculum Reinforcement Learning for Goal-Oriented Robot Control
    MEng Individual Project Imperial College London Department of Computing CuRL: Curriculum Reinforcement Learning for Goal-Oriented Robot Control Supervisor: Author: Dr. Ed Johns Harry Uglow Second Marker: Dr. Marc Deisenroth June 17, 2019 Abstract Deep Reinforcement Learning has risen to prominence over the last few years as a field making strong progress tackling continuous control problems, in particular robotic control which has numerous potential applications in industry. However Deep RL algorithms alone struggle on complex robotic control tasks where obstacles need be avoided in order to complete a task. We present Curriculum Reinforcement Learning (CuRL) as a method to help solve these complex tasks by guided training on a curriculum of simpler tasks. We train in simulation, manipulating a task environment in ways not possible in the real world to create that curriculum, and use domain randomisation in attempt to train pose estimators and end-to-end controllers for sim-to-real transfer. To the best of our knowledge this work represents the first example of reinforcement learning with a curriculum of simpler tasks on robotic control problems. Acknowledgements I would like to thank: • Dr. Ed Johns for his advice and support as supervisor. Our discussions helped inform many of the project’s key decisions. • My parents, Mike and Lyndsey Uglow, whose love and support has made the last four year’s possible. Contents 1 Introduction8 1.1 Objectives................................. 9 1.2 Contributions ............................... 10 1.3 Report Structure ............................. 11 2 Background 12 2.1 Machine learning (ML) .......................... 12 2.2 Artificial Neural Networks (ANNs) ................... 12 2.2.1 Strengths of ANNs .......................
    [Show full text]
  • Final Program of CCC2020
    第三十九届中国控制会议 The 39th Chinese Control Conference 程序册 Final Program 主办单位 中国自动化学会控制理论专业委员会 中国自动化学会 中国系统工程学会 承办单位 东北大学 CCC2020 Sponsoring Organizations Technical Committee on Control Theory, Chinese Association of Automation Chinese Association of Automation Systems Engineering Society of China Northeastern University, China 2020 年 7 月 27-29 日，中国·沈阳 July 27-29, 2020, Shenyang, China Proceedings of CCC2020 IEEE Catalog Number: CFP2040A -USB ISBN: 978-988-15639-9-6 CCC2020 Copyright and Reprint Permission: This material is permitted for personal use. For any other copying, reprint, republication or redistribution permission, please contact TCCT Secretariat, No. 55 Zhongguancun East Road, Beijing 100190, P. R. China. All rights reserved. Copyright@2020 by TCCT. 目录 (Contents) 目录 (Contents) ................................................................................................................................................... i 欢迎辞 (Welcome Address) ................................................................................................................................1 组织机构 (Conference Committees) ...................................................................................................................4 重要信息 (Important Information) ....................................................................................................................11 口头报告与张贴报告要求 (Instruction for Oral and Poster Presentations) .....................................................12 大会报告 (Plenary Lectures).............................................................................................................................14
    [Show full text]
  • Real-Time Vision, Tracking and Control
    Proceedings of the 2000 IEEE International Conference on Robotics & Automation San Francisco, CA April 2000 Real-Time Vision, Tracking and Control Peter I. Corke Seth A. Hutchinson CSIRO Manufacturing Science & Technology Beckman Institute for Advanced Technology Pinjarra Hills University of Illinois at Urbana-Champaign AUSTRALIA 4069. Urbana, Illinois, USA 61801 [email protected] [email protected] Abstract sidered the fusion of computer vision, robotics and This paper, which serves as an introduction to the control and has been a distinct field for over 10 years, mini-symposium on Real- Time Vision, Tracking and though the earliest work dates back close to 20 years. Control, provides a broad sketch of visual servoing, the Over this period several major, and well understood, approaches have evolved and been demonstrated in application of real-time vision, tracking and control many laboratories around the world. Fairly compre- for robot guidance. It outlines the basic theoretical approaches to the problem, describes a typical archi- hensive overviews of the basic approaches, current ap- tecture, and discusses major milestones, applications plications, and open research issues can be found in a and the significant vision sub-problems that must be number of recent sources, including [l-41. solved. The next section, Section 2, describes three basic ap- proaches to visual servoing. Section 3 provides a ‘walk 1 Introduction around’ the main functional blocks in a typical visual Visual servoing is a maturing approach to the control servoing system. Some major milestones and proposed applications are discussed in Section 4. Section 5 then of robots in which tasks are defined visually, rather expands on the various vision sub-problems that must than in terms of previously taught Cartesian coordi- be solved for the different approaches to visual servo- nates.
    [Show full text]
  • Arxiv:2011.00554V1 [Cs.RO] 1 Nov 2020 AI Agents [5]–[10]
    Can a Robot Trust You? A DRL-Based Approach to Trust-Driven Human-Guided Navigation Vishnu Sashank Dorbala, Arjun Srinivasan, and Aniket Bera University of Maryland, College Park, USA Supplemental version including Code, Video, Datasets at https://gamma.umd.edu/robotrust/ Abstract— Humans are known to construct cognitive maps of their everyday surroundings using a variety of perceptual inputs. As such, when a human is asked for directions to a particular location, their wayfinding capability in converting this cognitive map into directional instructions is challenged. Owing to spatial anxiety, the language used in the spoken instructions can be vague and often unclear. To account for this unreliability in navigational guidance, we propose a novel Deep Reinforcement Learning (DRL) based trust-driven robot navigation algorithm that learns humans’ trustworthiness to perform a language guided navigation task. Our approach seeks to answer the question as to whether a robot can trust a human’s navigational guidance or not. To this end, we look at training a policy that learns to navigate towards a goal location using only trustworthy human guidance, driven by its own robot trust metric. We look at quantifying various affective features from language-based instructions and incorporate them into our policy’s observation space in the form of a human trust metric. We utilize both these trust metrics into an optimal cognitive reasoning scheme that decides when and when not to trust the given guidance. Our results show that Fig. 1: We look at whether humans can be trusted on the naviga- the learned policy can navigate the environment in an optimal, tional guidance they give to a robot.
    [Show full text]
  • An Emotional Mimicking Humanoid Biped Robot and Its Quantum Control Based on the Constraint Satisfaction Model
    Portland State University PDXScholar Electrical and Computer Engineering Faculty Publications and Presentations Electrical and Computer Engineering 5-2007 An Emotional Mimicking Humanoid Biped Robot and its Quantum Control Based on the Constraint Satisfaction Model Quay Williams Portland State University Scott Bogner Portland State University Michael Kelley Portland State University Carolina Castillo Portland State University Martin Lukac Portland State University SeeFollow next this page and for additional additional works authors at: https:/ /pdxscholar.library.pdx.edu/ece_fac Part of the Electrical and Computer Engineering Commons, and the Robotics Commons Let us know how access to this document benefits ou.y Citation Details Williams Q., Bogner S., Kelley M., Castillo C., Lukac M., Kim D. H., Allen J., Sunardi M., Hossain S., Perkowski M. "An Emotional Mimicking Humanoid Biped Robot and its Quantum Control Based on the Constraint Satisfaction Model," 16th International Workshop on Post-Binary ULSI Systems, 2007 This Conference Proceeding is brought to you for free and open access. It has been accepted for inclusion in Electrical and Computer Engineering Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Authors Quay Williams, Scott Bogner, Michael Kelley, Carolina Castillo, Martin Lukac, Dong Hwa Kim, Jeff S. Allen, Mathias I. Sunardi, Sazzad Hossain, and Marek Perkowski This conference proceeding is available at PDXScholar: https://pdxscholar.library.pdx.edu/ece_fac/187 AN EMOTIONAL MIMICKING HUMANOID BIPED ROBOT AND ITS QUANTUM CONTROL BASED ON THE CONSTRAINT SATISFACTION MODEL Intelligent Robotics Laboratory, Portland State University Portland, Oregon. Quay Williams, Scott Bogner, Michael Kelley, Carolina Castillo, Martin Lukac, Dong Hwa Kim, Jeff Allen, Mathias Sunardi, Sazzad Hossain, and Marek Perkowski Abstract biped robots are very expensive, in range of hundreds The paper presents a humanoid robot that responds to thousands dollars.
    [Show full text]
  • Latent-Space Control with Semantic Constraints for Quadruped Locomotion
    First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion Alexander L. Mitchell1;2, Martin Engelcke1, Oiwi Parker Jones1, David Surovik2, Siddhant Gangapurwala2, Oliwier Melon2, Ioannis Havoutis2, and Ingmar Posner1 Abstract— Traditional approaches to quadruped control fre- quently employ simplified, hand-derived models. This signif- Constraint 1 icantly reduces the capability of the robot since its effective kinematic range is curtailed. In addition, kinodynamic con- y' straints are often non-differentiable and difficult to implement in an optimisation approach. In this work, these challenges are addressed by framing quadruped control as optimisation in a Robot Trajectory structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst x z x' complex dynamic and terminal constraints are expressed via high-level, semantic indicators and represented by learned classifiers operating upon the latent space. As a consequence, Initial robot configuration VAE and constraint complex constraints are rendered differentiable and evaluated predictors an order of magnitude faster than analytical approaches. We s' validate the feasibility of locomotion trajectories optimised us- Constraint 2 ing our approach both in simulation and on a real-world ANY- mal quadruped. Our results demonstrate that this approach is Constraint N capable of generating smooth and realisable trajectories. To the best of our knowledge, this is the first time latent space control Fig. 1: A VAE encodes the robot state and captures cor- has been successfully applied to a complex, real robot platform. relations therein in a structured latent space. Once trained, performance predictors (triangles) attached to the latent space predict if constraints are satisfied and apply arbitrarily I.
    [Show full text]
  • Mobile Robots Adaptive Control Using Neural Networks
    MOBILE ROBOTS ADAPTIVE CONTROL USING NEURAL NETWORKS I. Dumitrache, M. Dr ăgoicea University “Politehnica” Bucharest Faculty of Control and Computers Automatic Control and System Engineering Department Splaiul Independentei 313, 77206 - Bucharest, Romania Tel.+40 1 4119918, Fax: +40 1 4119918 E-mail: {idumitrache, mdragoicea}@ics.pub.ro Abstract: The paper proposes a feed-forward control strategy for mobile robot control that accounts for a non-linear model of the vehicle with interaction between inputs and outputs. It is possible to include specific model uncertainties in the dynamic model of the mobile robot in order to see how the control problem should be addressed taking into consideration the complete dynamic mobile robot model. By means of a neural network feed-forward controller a real non-linear mathematical model of the vehicle can be taken into consideration. The classical velocity control strategy can be extended using artificial neural networks in order to compensate for the modelling uncertainties. It is possible to develop an intelligent strategy for mobile robot control. Keywords: intelligent control systems, mobile robots, autonomous navigation, artificial neural networks. 1. INTRODUCTION finding, representation of space, control architecture). The last function, control architecture, Technological improvements in the design and embodies all the other functions to allow the agent to development of the mechanics and electronics of the move in its environment. systems have been followed by the development of very efficient and elaborate control strategies. The Robot autonomy requests today the leading edge of framework of mobile robotics is challenging from advanced robotics research. The variety of tasks to both theoretical and experimental point of view.
    [Show full text]
  • THE MOBILE ROBOT CONTROL for OBSTACLE AVOIDANCE with an ARTIFICIAL NEURAL NETWORK APPLICATION Victor Andreev, Victoria Tarasova
    30TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION DOI: 10.2507/30th.daaam.proceedings.099 THE MOBILE ROBOT CONTROL FOR OBSTACLE AVOIDANCE WITH AN ARTIFICIAL NEURAL NETWORK APPLICATION Victor Andreev, Victoria Tarasova This Publication has to be referred as: Andreev, V[ictor] & Tarasova, V[ictoria] (2019). The Mobile Robot Control for Obstacle Avoidance with an Artificial Neural Network Application, Proceedings of the 30th DAAAM International Symposium, pp.0724-0732, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-902734-22-8, ISSN 1726-9679, Vienna, Austria DOI: 10.2507/30th.daaam.proceedings.099 Abstract The article presents the results of the research, the purpose of which was to test the possibility of avoiding obstacles using the artificial neural network (ANN). The ANN functioning algorithm includes receiving data from ultrasonic sensors and control signal generation (direction vector), which goes to the Arduino UNO microcontroller responsible for mobile robot motors control. The software implementation of the algorithm was performed on the Iskra Neo microcontroller. The ANN learning mechanism is based on the Rumelhart-Hinton-Williams algorithm (back propagation). Keywords: Microcontroller; Arduino; ultra-sonic sensor; mobile robot; neural network; autonomous robot. 1. Introduction One of the most relevant tasks of modern robotics is the task of creating autonomous mobile robots that can navigate in space, i.e. make decisions in an existing, real environment. Path planning of mobile robots can be based on sensory signals from remote sensors [1]. Local planning is an implementation of this process. At present, the theory of artificial neural networks (ANNs) is increasingly being used to design motion control systems for mobile robots (MR).
    [Show full text]
  • AI-Perspectives: the Turing Option Frank Kirchner1,2
    Kirchner AI Perspectives (2020) 2:2 Al Perspectives https://doi.org/10.1186/s42467-020-00006-3 POSITION PAPER Open Access AI-perspectives: the Turing option Frank Kirchner1,2 Abstract This paper presents a perspective on AI that starts with going back to early work on this topic originating in theoretical work of Alan Turing. The argument is made that the core idea - that leads to the title of this paper - of these early thoughts are still relevant today and may actually provide a starting point to make the transition from today functional AI solutions towards integrative or general AI. Keywords: Artificial intelligence, AI technologies, Artificial neural networks, Machine learning, Intelligent robot interaction, Quantum computing Introduction introduce a special class which he called ‘unorganised When Alan Turing approached the topic of artificial Machines [1]’ and which has already anticipated many intelligence1 (AI) in the early first half of the last cen- features of the later developed artificial neural networks. tury, he did so on the basis of his work on the universal E.g. many very simple processing units which, as a cen- Turing machine which gave mankind a tool to calculate tral property, draw their computational power from the everything that can effectively be calculated. complexity of their connections and the resulting To take the next step and to think about AI seems al- interactions. most imperative in retrospect: if there are computational For as far sighted and visionary his ideas have been it phenomena on the hand then
    [Show full text]
  • Autonomous Driving: Planning, Control & Other Topics
    Autonomous Driving: Planning, Control & Other Topics Jan 8th , 2018 Sahil Narang University of North Carolina, Chapel Hill 1 Autonomous Driving: Main Components 2 LIDAR Interference Probability of LIDAR interference is very low LIDAR uses very focused light pulses Broad range of “Pulse Repetition Frequency” Most approaches fuse data from from various sensors Kim, G., Eom, J., & Park, Y. (2015, June). Investigation on the occurrence of mutual interference between pulsed terrestrial LIDAR scanners. In Intelligent Vehicles Symposium (IV), 2015 IEEE (pp. 437-442). IEEE. 3 Autonomous Driving: Main Components Perception collect information and extract relevant knowledge from the environment. 4 Autonomous Driving: Main Components Planning Making purposeful decisions in order to achieve the robot’s higher order goals 5 Autonomous Driving: Main Components Control Executing planned actions 6 Structure Modeling a car State Space Kinematic constraints Dynamic constraints Motion Planning Control Interaction with other Drivers Case Studies 7 Autonomous Driving: State Space “The set of attribute values describing the condition of an autonomous vehicle at an instance in time and at a particular place during its motion is termed the ‘state’ of the vehicle at that moment” Typically a vector with position, orientation, linear velocity, angular velocity State Space: set of all states the vehicle could occupy 8 Autonomous Driving: State Space Examples: 3D space with velocity (px, py, pz, θx, θy, θz, vx, vy, vz, ωx,ωy,ωz) (푝Ԧ, 휃Ԧ, 푣Ԧ, 휔) 2D space with acceleration (푝푥, 푝푦, 휃, 푣푥, 푣푦, 휔, 푎푥, 푎푦, 훼) 푝Ԧ, 휃, 푣Ԧ, 휔, 푎Ԧ, 훼 9 Autonomous Driving: State Space Examples: 2D space with blinker booleans 푝Ԧ, 휃, 푣Ԧ, 휔, 푏푙푙, 푏푙푟 State contains everything we need to describe the robot’s current configuration! Neglect some state variables when planning 10 Autonomous Driving: Holonomicity “Holonomic” robots Holonomic system are systems for which all constraints are integrable into positional constraints.
    [Show full text]