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High-dimensional complex motion generation is an interesting research topic. Most action generation methods in robotics research use a single pose as the model output. However, in some scenarios, only a series of motions can be output at one time. The calligraphy writing task belongs to a complex motion generation challenge which needs to output a series of motions at one time. The calligraphy writing task can be divided into position learning and posture learning. For position learning, human can directly form a properly rational statement of where to write. In Taylor's problem categories, the position learning problem in calligraphy learning belongs to Q3 and Q4 types which are formal statement. That is, human can easily design an algorithm to generate a policy to robot. In the contrast, humans are not able to describe the relationship between the writing posture and the writing result. Therefore, the posture learning problem in calligraphy learning belongs to Q1 and Q2 types in Taylor's problem categories. In order to solve the problems of Q1 and Q2, this dissertation will propose the fundamental cognitive system with self-learning ability. This dissertation integrates the framework of human perception, memory, and decision-making into the robot system through the cognitive psychology. We use the top-down and bottom-up processing of the human perceptual system to design a perception model of the cognitive system, which enables encoder networks to learn online. In the memory model, we implement the psychological multi-store model with a deep neural network, so that robots can remember past events like humans. We use the hypothesis generation model of psychology in the decision-making model, so that the robot has a human-like thinking process. Integrating these cognitive models, robots can generate action strategies based on their goals through their own experience. Finally, we use a practical robot as experimental platform to verify the learning ability of the proposed cognitive system.
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
TheInternationalSymposiumCreatingBrain-LikeIntelligencewasheldinFeb- ary 2007 in Germany. The symposium brought together notable scientists from di?erent backgrounds and with di?erent expertise related to the emerging ?eld of brain-like intelligence. Our understanding of the principles behind brain-like intelligence is still limited. After all, we have had to acknowledge that after tremendous advances in areas like neural networks, computational and arti?cial intelligence (a ?eld that had just celebrated its 50 year anniversary) and fuzzy systems, we are still not able to mimic even the lower-level sensory capabilities of humans or animals. We asked what the biggest obstacles are and how we could gain ground toward a scienti?c understanding of the autonomy, ?exibility, and robustness of intelligent biological systems as they strive to survive. New principles are usually found at the interfaces between existing disciplines, and traditional boundaries between disciplines have to be broken down to see how complex systems become simple and how the puzzle can be assembled. During the symposium we could identify some recurring themes that p- vaded many of the talks and discussions. The triad of structure, dynamics and environment,theroleoftheenvironmentasanactivepartnerinshapingsystems, adaptivity on all scales (learning, development, evolution) and the amalga- tion of an internal and external world in brain-like intelligence rate high among them. Each of us is rooted in a certain community which we have to serve with the results of our research. Looking beyond our ?elds and working at the interfaces between established areas of research requires e?ort and an active process.
This state-of-the-art survey contains selected papers contributed by researchers in intelligent systems, cognitive robotics, and neuroscience including contributions from the MirrorBot project and from the NeuroBotics Workshop 2004. The research work presented demonstrates significant novel developments in biologically inspired neural models for use in intelligent robot environments and biomimetic cognitive behavior.
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
This book describes how to create robots capable to develop the behavioral and cognitive skills required to perform a task through machine learning methods. It focuses on model-free approaches with minimal human intervention in which the behavior used by the robots to solve their task and the way in which such behavior is produced is discovered by the adaptive process automatically, i.e. it is not specified by the experimenter. The book, which is targeted toward researchers, PhD and Master students with an interest in machine learning and robotics: (i) introduces autonomous robots, evolutionary algorithms, reinforcement learning algorithms, and learning by demonstration methods, (ii) uses concrete experiments to illustrate the fundamental aspects of embodied intelligence, (iii) provides theoretical and practical knowledge, including tutorials and exercises, and (iv) provides an integrated review of recent research in this area carried within partially separated research communities.
Mobile Robotics: A Practical Introduction (2nd edition) is an excellent introduction to the foundations and methods used for designing completely autonomous mobile robots. A fascinating, cutting-edge, research topic, autonomous mobile robotics is now taught in more and more universities. In this book you are introduced to the fundamental concepts of this complex field via twelve detailed case studies that show how to build and program real working robots. Topics covered in clued learning, autonomous navigation in unmodified, noisy and unpredictable environments, and high fidelity robot simulation. This new edition has been updated to include a new chapter on novelty detection, and provides a very practical introduction to mobile robotics for a general scientific audience. It is essential reading for 2nd and 3rd year undergraduate students and postgraduate students studying robotics, artificial intelligence, cognitive science and robot engineering. The update and overview of core concepts in mobile robotics will assist and encourage practitioners of the field and set challenges to explore new avenues of research in this exiting field. The author is Senior Lecturer at the Department of Computer Science at the University of Essex. "A very fine overview over the relevant problems to be solved in the attempt to bring intelligence to a moving vehicle." Professor Dr. Ewald von Puttkamer, University of Kaiserslautern "Case studies show ways of achieving an impressive repertoire of kinds of learned behaviour, navigation and map-building. The book is an admirable introduction to this modern approach to mobile robotics and certainly gives a great deal of food for thought. This is an important and though-provoking book." Alex M. Andrew in Kybernetes Vol 29 No 4 and Robotica Vol 18
Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.
Robots are becoming more human, but could they also become sentient and have human-like consciousness?What is consciousness, exactly?It is a fact that our thoughts and consciousness are based on the neural activity of the brain. It is also a fact that we do not perceive our brain activity as it really is — patterns of neural firings. Instead, we perceive our sensations and thoughts apparently as they are. What kind of condition would transform the neural activity into this kind of internal appearance? This is the basic problem of consciousness.The author proposes an explanation that also provides preconditions for true conscious cognition — the requirement of a direct perceptive system with inherent sub-symbolic and symbolic information processing. Associative neural information processing with distributed signal representations is introduced as a method that satisfies these requirements.Conscious robot cognition also calls for information integration and sensorimotor integration. This requirement is satisfied by the Haikonen Cognitive Architecture (HCA).This book demystifies both the enigmatic philosophical issues of consciousness and the practical engineering issues of conscious robots by presenting them in an easy-to-understand manner for the benefit of students, researchers, philosophers and engineers in the field.