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Author: Blooma Goldberg
Publisher:
ISBN: 9780996261623
Category :
Languages : en
Pages :
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Book Description
Can robots learn? Blooma and her friends in the Razzle-Dazzle Robot Club hope so. They build a robot and try to train it to clean up their workshop, but that turns out to be harder than it sounds. Will Clark the Cleaning Robot ever learn to clean up?
Author: Blooma Goldberg
Publisher:
ISBN: 9780996261623
Category :
Languages : en
Pages :
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Book Description
Can robots learn? Blooma and her friends in the Razzle-Dazzle Robot Club hope so. They build a robot and try to train it to clean up their workshop, but that turns out to be harder than it sounds. Will Clark the Cleaning Robot ever learn to clean up?
Author: Aude Billard
Publisher: MIT Press
ISBN: 0262367017
Category : Technology & Engineering
Languages : en
Pages : 425
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Book Description
Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.
Author: Joseph E. Aoun
Publisher: MIT Press
ISBN: 0262549859
Category : Education
Languages : en
Pages : 221
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Book Description
A fresh look at a “robot-proof” education in the new age of generative AI. In 2017, Robot-Proof, the first edition, foresaw the advent of the AI economy and called for a new model of higher education designed to help human beings flourish alongside smart machines. That economy has arrived. Creative tasks that, seven years ago, seemed resistant to automation can now be performed with a simple prompt. As a result, we must now learn not only to be conversant with these technologies, but also to comprehend and deploy their outputs. In this revised and updated edition, Joseph Aoun rethinks the university’s mission for a world transformed by AI, advocating for the lifelong endeavor of a “robot-proof” education. Aoun puts forth a framework for a new curriculum, humanics, which integrates technological, data, and human literacies in an experiential setting, and he renews the call for universities to embrace lifelong learning through a social compact with government, employers, and learners themselves. Drawing on the latest developments and debates around generative AI, Robot-Proof is a blueprint for the university as a force for human reinvention in an era of technological change—an era in which we must constantly renegotiate the shifting boundaries between artificial intelligence and the capacities that remain uniquely human.
Author: Florian Golemo
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Robots are the future. But how can we teach them useful new skills? This work covers a variety of topics, all with the common goal of making it easier to train robots. The first main component of this thesis is our work on model-building sim2real transfer. When a policy has been learned entirely in simulation, the performance of this policy is usually drastically lower on the real robot. This can be due to random noise, to imprecisions, or to unmodelled effects like backlash. We introduce a new technique for learning the discrepancy between the simulator and the real robot and using this discrepancy to correct the simulator. We found that for several of our ideas there weren't any suitable simulations available. Therefore, for the second main part of the thesis, we created a set of new robotic simulation and test environments. We provide (1) several new robot simulations for existing robots and variations on existing environments that allow for rapid adjustment of the robot dynamics. We also co-created (2) the Duckietown AIDO challenge, which is a large scale live robotics competition for the conferences NIPS 2018 and ICRA 2019. For this challenge we created the simulation infrastructure, which allows participants to train their robots in simulation with or without ROS. It also lets them evaluate their submissions automatically on live robots in a ”Robotarium”. In order to evaluate a robot's understanding and continuous acquisition of language, we developed the (3) Multimodal Human-Robot Interaction benchmark (MHRI). This test set contains several hours of annotated recordings of different humans showing and pointing at common household items, all from a robot's perspective. The novelty and difficulty in this task stems from the realistic noise that is included in the dataset: Most humans were non-native English speakers, some objects were occluded and none of the humans were given any detailed instructions on how to communicate with the robot, resulting in very natural interactions. After completing this benchmark, we realized the lack of simulation environments that are sufficiently complex to train a robot for this task. This would require an agent in a realistic house settings with semantic annotations. That is why we created (4) HoME, a platform for training household robots to understand language. The environment was created by wrapping the existing SUNCG 3D database of houses in a game engine to allow simulated agents to traverse the houses. It integrates a highly-detailed acoustic engine and a semantic engine that can generate object descriptions in relation to other objects, furniture, and rooms. The third and final main contribution of this work considered that a robot might find itself in a novel environment which wasn't covered by the simulation. For such a case we provide a new approach that allows the agent to reconstruct a 3D scene from 2D images by learning object embeddings, since especially in low-cost robots a depth sensor is not always available, but 2D cameras a common. The main drawback of this work is that it currently doesn't reliably support reconstruction of color or texture. We tested the approach on a mental rotation task, which is common in IQ tests, and found that our model performs significantly better in recognizing and rotating objects than several baselines.
Author: Alexandros Iosifidis
Publisher: Academic Press
ISBN: 0323885721
Category : Technology & Engineering
Languages : en
Pages : 638
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Book Description
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Author: Christine Zuchora-Walske
Publisher: Lerner Publications ™
ISBN: 1541508971
Category : Juvenile Nonfiction
Languages : en
Pages : 33
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Book Description
Robots work in homes every day. Some vacuum floors or mow lawns. Others keep people company. And some help kids have fun! What robots might you find in someone's home? Read this book to find out!
Author: Marco Dorigo
Publisher: MIT Press
ISBN: 9780262041645
Category : Computers
Languages : en
Pages : 238
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Book Description
foreword by Lashon Booker To program an autonomous robot to act reliably in a dynamic environment is a complex task. The dynamics of the environment are unpredictable, and the robots' sensors provide noisy input. A learning autonomous robot, one that can acquire knowledge through interaction with its environment and then adapt its behavior, greatly simplifies the designer's work. A learning robot need not be given all of the details of its environment, and its sensors and actuators need not be finely tuned. Robot Shaping is about designing and building learning autonomous robots. The term "shaping" comes from experimental psychology, where it describes the incremental training of animals. The authors propose a new engineering discipline, "behavior engineering," to provide the methodologies and tools for creating autonomous robots. Their techniques are based on classifier systems, a reinforcement learning architecture originated by John Holland, to which they have added several new ideas, such as "mutespec," classifier system "energy,"and dynamic population size. In the book they present Behavior Analysis and Training (BAT) as an example of a behavior engineering methodology.
Author: Lloyd Brombach
Publisher: BPB Publications
ISBN: 9389423465
Category : Technology & Engineering
Languages : en
Pages : 501
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Book Description
Learn how to build and program real autonomous robots KEY FEATURES _ÊSimplified coverage on fundamentals of building a robot platform. _ÊLearn to program Raspberry Pi for interacting with hardware. _ÊCutting-edge coverage on autonomous motion, mapping, and path planning algorithms for advanced robotics. Ê DESCRIPTION Practical Robotics in C++ teaches the complete spectrum of Robotics, right from the setting up a computer for a robot controller to putting power to the wheel motors. The book brings you the workshop knowledge of the electronics, hardware, and software for building a mobile robot platform.Ê You will learn how to use sensors to detect obstacles, how to train your robot to build itself a map and plan an obstacle-avoiding path, and how to structure your code for modularity and interchangeability with other robot projects. Throughout the book, you can experience the demonstrations ofÊcomplete coding of robotics with the use of simple and clear C++ programming. In addition, you will explore how to leverage the Raspberry Pi GPIO hardware interface pins and existing libraries to make an incredibly capable machine on the most affordable computer platform ever. Ê WHAT YOU WILL LEARN Ê _ÊWrite code for the motor drive controller. _ÊBuild a Map from Lidar Data. _ÊWrite and implement your own autonomous path-planning algorithm. _ÊWrite code to send path waypoints to the motor drive controller autonomously. _ÊGet to know more about robot mapping and navigation.Ê WHO THIS BOOK IS FOR This book is most suitable for C++ programmers who have keen interest in robotics and hardware programming. All you need is just a good understanding of C++ programming to get the most out of this book. Ê TABLE OF CONTENTS 1. Choose and Set Up a Robot Computer 2. GPIO Hardware Interface Pins Overview and Use 3. The Robot Platform 4. Types of Robot Motors and Motor Control 5. Communication with Sensors and other Devices 6. Additional Helpful Hardware 7. Adding the Computer to Control your Robot 8. Robot Control Strategy 9. Coordinating the Parts 10. Maps for Robot Navigation 11. Robot Tracking and Localization 12. Autonomous Motion 13. Autonomous Path Planning 14. Wheel Encoders for Odometry 15. Ultrasonic Range Detectors 16. IMUs: Accelerometers, Gyroscopes, and Magnetometers 17. GPS and External Beacon Systems 18. LIDAR Devices and Data 19. Real Vision with Cameras 20. Sensor Fusion 21. Building and Programming an Autonomous Robot
Author: Sudharsan Ravichandiran
Publisher: Packt Publishing Ltd
ISBN: 178883691X
Category : Computers
Languages : en
Pages : 309
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Book Description
A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features Your entry point into the world of artificial intelligence using the power of Python An example-rich guide to master various RL and DRL algorithms Explore various state-of-the-art architectures along with math Book Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence. What you will learn Understand the basics of reinforcement learning methods, algorithms, and elements Train an agent to walk using OpenAI Gym and Tensorflow Understand the Markov Decision Process, Bellman’s optimality, and TD learning Solve multi-armed-bandit problems using various algorithms Master deep learning algorithms, such as RNN, LSTM, and CNN with applications Build intelligent agents using the DRQN algorithm to play the Doom game Teach agents to play the Lunar Lander game using DDPG Train an agent to win a car racing game using dueling DQN Who this book is for If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
Author: Francis X. Govers
Publisher: Packt Publishing Ltd
ISBN: 1788835700
Category : Computers
Languages : en
Pages : 336
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Book Description
Bring a new degree of interconnectivity to your world by building your own intelligent robots Key Features Leverage fundamentals of AI and robotics Work through use cases to implement various machine learning algorithms Explore Natural Language Processing (NLP) concepts for efficient decision making in robots Book DescriptionArtificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills. As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks. By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence.What you will learn Get started with robotics and artificial intelligence Apply simulation techniques to give your robot an artificial personality Understand object recognition using neural networks and supervised learning techniques Pick up objects using genetic algorithms for manipulation Teach your robot to listen using NLP via an expert system Use machine learning and computer vision to teach your robot how to avoid obstacles Understand path planning, decision trees, and search algorithms in order to enhance your robot Who this book is for If you have basic knowledge about robotics and want to build or enhance your existing robot’s intelligence, then Artificial Intelligence for Robotics is for you. This book is also for enthusiasts who want to gain knowledge of AI and robotics.
