Download or read book Robot Learning from Interactions with Physics realistic Environment Constructing Big Task Platform for Training AI Agents written by Xu Xie and published by . This book was released on 2021 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot learning from interactions is a crucial topic in the joint field of computer vision, robotics, and machine learning. Interactions are ubiquitous in daily life, concrete instances comprise object-object, robot-object, and robot-robot interactions. Learning from interactions to an intelligent robot system is important because it helps the robot to generate a sense of physics, meanwhile planning and acting reasonably. To achieve this purpose, one primary challenge that remains in the community is the absence of dataset that can be leveraged to study the diverse categories of interactions. To create those datasets, the interaction data should be realistic such that it reflects the underlying physical process. Further, we argue that learning interactions through simulations is a promising approach to synthesize and scale up diverse forms of interactions. This dissertation focuses on robot learning from interactions in Mixed Reality (MR) as well as leveraging the state-of-the-art physical simulation to construct virtual environments to afford Big Tasks. There are four major contributions along this pathway: 1. Robot learning object manipulation skills from human demonstrations. Instead of directly learning from a robot-object manipulation dataset that is hard to generalize, we alternatively seek an approach to create a human-object manipulation dataset and let the robot learn from the demonstration. We claim that the key attribute of building such dataset embodies the realistic hand-object interaction that involves a setup that can faithfully capture the fine-grained raw motion signals. This leads us to develop a tactile glove system and collect informative spatial-temporal sensory data during hand manipulations. An event parsing pipeline is proposed upon the hand interactions that are transferable to the robot's end and learn the manipulation skill. 2. A virtual testbed to construct rich interactive tasks. The major limitation of collecting real-world interaction data can be summarized as three folds: i) a specific setup is needed to trace one form of interaction, ii) amount of efforts need to spend on data cleaning and labeling, and iii) a single dataset is not capable to capture different modalities of interactions at the same time. To overcome those issues, we propose and develop a virtual testbed, VRGym platform, for realistic human-robot interactive tasks (Big Tasks). In VRGym, the pipelines we developed are able to synthesize diverse photo-realistic 3D scenes that incorporate various forms of interactions through physics-based simulation. Given available rich interactions, we expect to grow a general-purpose agent from the interactive tasks and advance the research areas of robotics, machine learning as well as cognitive science. 3. Robot learning from imperfect demonstrations --- small data. In the area of learning from demonstration, interacting with objects, one essential element is the creation of expert demonstrations. However, non-trivial efforts are needed when collecting those demonstrations and a large portion of them contains failure cases. We develop the demonstration setup for learning objects grasping skills upon VRGym platform with VR human interfaces. Human performers interact with the virtual scene by teleoperating the virtual robot arm. At the same time, the demonstration is evaluated through physics simulation such that even a perfect task plan may fail during the execution. Given the sparsity of demonstrations, we think the failed ones are valuable in addition to the perfect demonstration. This enlightens us to exploit the implicit characteristics of small data in the presence of imperfect demonstrations. 4. A game platform for large-scale social interactions. Social interactions are another important branch that goes beyond physical only interactions. To develop a general-purpose agent, it has to properly infer other agents motion or intentions and applies socially acceptable behaviors when interacting in the scene. Inspired by those facts, we leverage a popular computer game platform, Grand Theft Auto (GTA), to automatically construct fruitful realistic social interactions in the simulated urban scenarios. The city transportation system, including vehicles and pedestrians, can be fully controlled by the developed modding scripts. The GTA platform is a supplement to VRGym that extends robot learning from interactions to a larger scale. We utilize it to synthesize multi-vehicle driving scenarios and study the problem of trajectories prediction as to the basis of intentions inference. We highlight the safety aspect by predicting collision-free trajectories that accord with the social norm for vehicle driving.
Download or read book Making Robots Smarter written by Katharina Morik and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making Robots Smarter is a book about learning robots. It treats this topic based on the idea that the integration of sensing and action is the central issue. In the first part of the book, aspects of learning in execution and control are discussed. Methods for the automatic synthesis of controllers, for active sensing, for learning to enhance assembly, and for learning sensor-based navigation are presented. Since robots are not isolated but should serve us, the second part of the book discusses learning for human-robot interaction. Methods of learning understandable concepts for assembly, monitoring, and navigation are described as well as optimizing the implementation of such understandable concepts for a robot's real-time performance. In terms of the study of embodied intelligence, Making Robots Smarter asks how skills are acquired and where capabilities of execution and control come from. Can they be learned from examples or experience? What is the role of communication in the learning procedure? Whether we name it one way or the other, the methodological challenge is that of integrating learning capabilities into robots.
Download or read book Toward Learning Robots written by Walter Van de Velde and published by MIT Press. This book was released on 1993 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributions in Toward Learning Robots address the question of how a robot can be designed to acquire autonomously whatever it needs to realize adequate behavior in a complex environment. In-depth discussions of issues, techniques, and experiments in machine learning focus on improving ease of programming and enhancing robustness in unpredictable and changing environments, given limitations of time and resources available to researchers. The authors show practical progress toward a useful set of abstractions and techniques to describe and automate various aspects of learning in autonomous systems. The close interaction of such a system with the world reveals opportunities for new architectures and learning scenarios and for grounding symbolic representations, though such thorny problems as noise, choice of language, abstraction level of representation, and operationality have to be faced head-on. Contents Introduction: Toward Learning Robots * Learning Reliable Manipulation Strategies without Initial Physical Models * Learning by an Autonomous Agent in the Pushing Domain * A Cost-Sensitive Machine Learning Method for the Approach and Recognize Task * A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations * Understanding Object Motion: Recognition, Learning and Spatiotemporal Reasoning * Learning How to Plan * Robo-Soar: An Integration of External Interaction, Planning, and Learning Using Soar * Foundations of Learning in Autonomous Agents * Prior Knowledge and Autonomous Learning
Download or read book Robot Learning from Human Demonstration written by Sonia Dechter and published by Springer Nature. This book was released on 2022-06-01 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.
Download or read book Robot Learning Human Skills and Intelligent Control Design written by Chenguang Yang and published by CRC Press. This book was released on 2021-06-21 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user’s arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.
Download or read book Training and Deploying Visual Agents at Scale written by Linxi Fan and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous agents that perceive and interact with the world, such as home robots and self-driving vehicles, hold great promises to a future that automates mundane tasks and improves the living standards for billions of people. However, two major obstacles stand in our way towards this grand goal. First, modern AI systems require huge amount of data to learn meaningful behaviors, yet training them directly on physics robots is unscalable due to high cost and low efficiency. Second, mobile robot platforms typically have limited onboard computing resources but demand low reaction latency, which hinders the mass deployment of large-capacity visual models. In this dissertation, we will explore an effective recipe towards developing algorithms and systems that are able to train and deploy visual agents at scale. The key idea is to train the agents in rich simulation, then overcome the sim-to-real gap, and finally deploy efficiently on edge devices with lightweight video processing architectures. This dissertation is organized around 4 primary components in the pipeline. First, we propose an open-source distributed framework that provides a full-stack solution to accelerate reinforcement learning (RL) significantly for complex robotics tasks. Second, we construct an ecologically valid and visually realistic simulator for home robotic tasks. Third, we introduce a novel policy learning method that achieves zero-shot generalization to unseen visual environments with large distributional shifts, which facilitates sim-to-real transfer. Finally, we design a new family of video learning architectures that enables deep video understanding for visual agents on resource-constrained devices. We hope that the techniques and ideas presented in this dissertation will bring us one step closer to the future where intelligent robots will become as ubiquitous as smartphones in our lives.
Download or read book Large scale Simulation for Embodied Perception and Robot Learning written by Fei Xia (Researcher in computer vision) and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Being able to perceive and interact with complex human environments is an important yet challenging problem in robotics for decades. Learning active perception and sensorimotor control by interacting with the physical world is cumbersome as existing algorithms are too slow to learn in real-time, and robots are fragile and costly. This has given rise to learning in simulation, and to make progress on this problem, efficient simulation infrastructure needs to be developed to support interactive and long-horizon tasks, and sample-efficient learning algorithms need to be developed to solve these tasks. In this dissertation, I present two lines of work contributing to these topics. The first line of work is to create large-scale, realistic, and interactive simulation environments, including Gibson Environment and iGibson. Gibson Environment is proposed for learning real-world perception for active agents. Gibson Environment is built from the real world and reflects its semantic complexity. It has a neural network-based renderer and a mechanism named ``Goggle" to ensure no need to further domain adaptation before deployment of results in the real world. Gibson Environment significantly improves pixel-level realism over existing simulation environments. To build upon Gibson Environment and improve the physical realism of the simulation, I propose iGibson, a simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. The simulated scenes are replicas of 3D scanned real-world homes, aligning the distribution of objects and layout to those of the real world. Novel long horizon problems including interactive navigation and mobile manipulation can be defined in this environment, and I show evidence that solutions can be transferred to the real world. The second line of work studies reinforcement learning (RL) for long-horizon robotics problems enabled by the interactive simulation environments. First, I introduce the interactive navigation problem and associated metrics. I leverage model-free RL algorithms to solve the proposed interactive navigation problems. Second, to solve challenging tasks in fully interactive simulation environments and improve sample efficiency of RL, I propose ReLMoGen, a framework to integrate motion generation into RL. I propose to lift the action space from joint control signals to motion generation subgoals. By lifting the action space and leveraging sampling-based motion planners, I can efficiently use RL to solve complex long-horizon tasks that existing RL methods cannot solve in the original action space.
Download or read book Robot Physical Interaction through the combination of Vision Tactile and Force Feedback written by Mario Prats and published by Springer. This book was released on 2012-10-05 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot manipulation is a great challenge; it encompasses versatility -adaptation to different situations-, autonomy -independent robot operation-, and dependability -for success under modeling or sensing errors. A complete manipulation task involves, first, a suitable grasp or contact configuration, and the subsequent motion required by the task. This monograph presents a unified framework by introducing task-related aspects into the knowledge-based grasp concept, leading to task-oriented grasps. Similarly, grasp-related issues are also considered during the execution of a task, leading to grasp-oriented tasks which is called framework for physical interaction (FPI). The book presents the theoretical framework for the versatile specification of physical interaction tasks, as well as the problem of autonomous planning of these tasks. A further focus is on sensor-based dependable execution combining three different types of sensors: force, vision and tactile. The FPI approach allows to perform a wide range of robot manipulation tasks. All contributions are validated with several experiments using different real robots placed on household environments; for instance, a high-DoF humanoid robot can successfully operate unmodeled mechanisms with widely varying structure in a general way with natural motions. This research was recipient of the European Georges Giralt Award and the Robotdalen Scientific Award Honorary Mention.
Download or read book Interactive Task Learning written by Kevin A. Gluck and published by National Geographic Books. This book was released on 2019-09-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experts from a range of disciplines explore how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other. Humans are not limited to a fixed set of innate or preprogrammed tasks. We learn quickly through language and other forms of natural interaction, and we improve our performance and teach others what we have learned. Understanding the mechanisms that underlie the acquisition of new tasks through natural interaction is an ongoing challenge. Advances in artificial intelligence, cognitive science, and robotics are leading us to future systems with human-like capabilities. A huge gap exists, however, between the highly specialized niche capabilities of current machine learning systems and the generality, flexibility, and in situ robustness of human instruction and learning. Drawing on expertise from multiple disciplines, this Strüngmann Forum Report explores how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other. The contributors consider functional knowledge requirements, the ontology of interactive task learning, and the representation of task knowledge at multiple levels of abstraction. They explore natural forms of interactions among humans as well as the use of interaction to teach robots and software agents new tasks in complex, dynamic environments. They discuss research challenges and opportunities, including ethical considerations, and make proposals to further understanding of interactive task learning and create new capabilities in assistive robotics, healthcare, education, training, and gaming. Contributors Tony Belpaeme, Katrien Beuls, Maya Cakmak, Joyce Y. Chai, Franklin Chang, Ropafadzo Denga, Marc Destefano, Mark d'Inverno, Kenneth D. Forbus, Simon Garrod, Kevin A. Gluck, Wayne D. Gray, James Kirk, Kenneth R. Koedinger, Parisa Kordjamshidi, John E. Laird, Christian Lebiere, Stephen C. Levinson, Elena Lieven, John K. Lindstedt, Aaron Mininger, Tom Mitchell, Shiwali Mohan, Ana Paiva, Katerina Pastra, Peter Pirolli, Roussell Rahman, Charles Rich, Katharina J. Rohlfing, Paul S. Rosenbloom, Nele Russwinkel, Dario D. Salvucci, Matthew-Donald D. Sangster, Matthias Scheutz, Julie A. Shah, Candace L. Sidner, Catherine Sibert, Michael Spranger, Luc Steels, Suzanne Stevenson, Terrence C. Stewart, Arthur Still, Andrea Stocco, Niels Taatgen, Andrea L. Thomaz, J. Gregory Trafton, Han L. J. van der Maas, Paul Van Eecke, Kurt VanLehn, Anna-Lisa Vollmer, Janet Wiles, Robert E. Wray III, Matthew Yee-King
Download or read book AI based Robot Safe Learning and Control written by Xuefeng Zhou and published by Springer Nature. This book was released on 2020-06-02 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.
Download or read book How to Train Your Robot New Environments for Robotic Training and New Methods for Transferring Policies from the Simulator to the Real Robot written by Florian Golemo and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Robot Learning written by J. H. Connell and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.
Download or read book Robot Learning by Visual Observation written by Aleksandar Vakanski and published by John Wiley & Sons. This book was released on 2017-01-13 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstraction Discusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problem Focuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regression Concentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert
Download or read book Reinforcement Learning of Bimanual Robot Skills written by Adrià Colomé and published by Springer Nature. This book was released on 2019-08-27 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book tackles all the stages and mechanisms involved in the learning of manipulation tasks by bimanual robots in unstructured settings, as it can be the task of folding clothes. The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the learning process. It proposes practical enhancements to closed-loop inverse kinematics for redundant robots, a procedure to position the two arms to maximize workspace manipulability, and a dynamic model together with a disturbance observer to achieve compliant control and safe robot behavior. In the second part, methods for robot motion learning based on movement primitives and direct policy search algorithms are presented. To improve sampling efficiency and accelerate learning without deteriorating solution quality, techniques for dimensionality reduction, for exploiting low-performing samples, and for contextualization and adaptability to changing situations are proposed. In sum, the reader will find in this comprehensive exposition the relevant knowledge in different areas required to build a complete framework for model-free, compliant, coordinated robot motion learning.
Download or read book Artificial Intelligence for Robotics written by Francis X. Govers III and published by Packt Publishing Ltd. This book was released on 2024-03-29 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Let an AI and robotics expert help you apply AI, systems engineering, and ML concepts to create smart robots capable of interacting with their environment and users, making decisions, and navigating autonomously Key Features Gain a holistic understanding of robot design, systems engineering, and task analysis Implement AI/ML techniques to detect and manipulate objects and navigate robots using landmarks Integrate voice and natural language interactions to create a digital assistant and artificial personality for your robot Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnlock the potential of your robots by enhancing their perception with cutting-edge artificial intelligence and machine learning techniques. From neural networks to computer vision, this second edition of the book equips you with the latest tools, new and expanded topics such as object recognition and creating artificial personality, and practical use cases to create truly smart robots. Starting with robotics basics, robot architecture, control systems, and decision-making theory, this book presents systems-engineering methods to design problem-solving robots with single-board computers. You'll explore object recognition using YOLO and genetic algorithms to teach your robot to identify and pick up objects, leverage natural language processing to give your robot a voice, and master neural networks to classify and separate objects and navigate autonomously, before advancing to guiding your robot arms using reinforcement learning and genetic algorithms. The book also covers path planning and goal-oriented programming to prioritize your robot's tasks, showing you how to connect all software using Python and ROS 2 for a seamless experience. By the end of this book, you'll have learned how to transform your robot into a helpful assistant with NLP and give it an artificial personality, ready to tackle real-world tasks and even crack jokes.What you will learn Get started with robotics and AI essentials Understand path planning, decision trees, and search algorithms to enhance your robot Explore object recognition using neural networks and supervised learning techniques Employ genetic algorithms to enable your robot arm to manipulate objects Teach your robot to listen using Natural Language Processing through an expert system Program your robot in how to avoid obstacles and retrieve objects with machine learning and computer vision Apply simulation techniques to give your robot an artificial personality Who this book is for This book is for practicing robotics engineers and enthusiasts aiming to advance their skills by applying AI and ML techniques. Students and researchers looking for practical guidance for solving specific problems or approaching a difficult robot design will find this book insightful. Proficiency in Python programming, familiarity with electronics and wiring, single board computers, Linux-based command-line interface (CLI), and knowledge of AI/ML concepts are required to get started with this book.
Download or read book Learning Based Robot Vision written by Josef Pauli and published by Springer. This book was released on 2001-05-09 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Industrial robots carry out simple tasks in customized environments for which it is typical that nearly all e?ector movements can be planned during an - line phase. A continual control based on sensory feedback is at most necessary at e?ector positions near target locations utilizing torque or haptic sensors. It is desirable to develop new-generation robots showing higher degrees of autonomy for solving high-level deliberate tasks in natural and dynamic en- ronments. Obviously, camera-equipped robot systems, which take and process images and make use of the visual data, can solve more sophisticated robotic tasks. The development of a (semi-) autonomous camera-equipped robot must be grounded on an infrastructure, based on which the system can acquire and/or adapt task-relevant competences autonomously. This infrastructure consists of technical equipment to support the presentation of real world training samples, various learning mechanisms for automatically acquiring function approximations, and testing methods for evaluating the quality of the learned functions. Accordingly, to develop autonomous camera-equipped robot systems one must ?rst demonstrate relevant objects, critical situations, and purposive situation-action pairs in an experimental phase prior to the application phase. Secondly, the learning mechanisms are responsible for - quiring image operators and mechanisms of visual feedback control based on supervised experiences in the task-relevant, real environment. This paradigm of learning-based development leads to the concepts of compatibilities and manifolds. Compatibilities are general constraints on the process of image formation which hold more or less under task-relevant or accidental variations of the imaging conditions.
Download or read book A Hybrid Deliberative Layer for Robotic Agents written by Ronny Hartanto and published by Springer. This book was released on 2011-07-18 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Hybrid Deliberative Layer (HDL) solves the problem that an intelligent agent faces in dealing with a large amount of information which may or may not be useful in generating a plan to achieve a goal. The information, that an agent may need, is acquired and stored in the DL model. Thus, the HDL is used as the main knowledge base system for the agent. In this work, a novel approach which amalgamates Description Logic (DL) reasoning with Hierarchical Task Network (HTN) planning is introduced. An analysis of the performance of the approach has been conducted and the results show that this approach yields significantly smaller planning problem descriptions than those generated by current representations in HTN planning.
