Dynamic Neuromechanical Sets for Locomotion

Most biological systems employ multiple redundant actuators, which is a complicated problem of controls and analysis. Unless assumptions about how the brain and body work together, and assumptions about how the body prioritizes tasks are applied, it is not possible to find the actuator controls. The purpose of this research is to develop computational tools for the analysis of arbitrary musculoskeletal models that employ redundant actuators. Instead of relying primarily on optimization frameworks and numerical methods or task prioritization schemes used typically in biomechanics to find a singular solution for actuator controls, tools for feasible sets analysis are instead developed to find the bounds of possible actuator controls. Previously in the literature, feasible sets analysis has been used in order analyze models assuming static poses. Here, tools that explore the feasible sets of actuator controls over the course of a dynamic task are developed. The cost-function agnostic methods of analysis developed in this work run parallel and in concert with other methods of analysis such as principle components analysis, muscle synergies theory and task prioritization. Researchers and healthcare professionals can gain greater insights into decision making during behavioral tasks by layering these other tools on top of feasible sets analysis

Achieving human-like dexterity in robotic hands : inspiration from human hand biomechanics and neuromuscular control

The human hand's unique biomechanical structure and neuromuscular control combine to produce amazing dexterous capabilities in a way that is still not fully understood. The Anatomically Correct Testbed (ACT) hand is a robotic system that is designed as a physical simulation of the human hand, and can help us examine and potentially uncover the roles of biomechanics and neural control in achieving dexterity. In this dissertation, I utilize the ACT hand and other robotic systems to explore the underlying sources of human hand dexterity, with the goal of understanding the fundamental differences between robotic and human hands in terms of (i) mechanical joint/tendon structure and (ii) control strategies. To begin, I develop comprehensive mechanical models that describe the musculoskeletal and tendon mechanics of the fingers and thumb of the human hand. Then, I work to isolate the contributions of biomechanical structure and neuromuscular control toward human dexterity. I have developed and implemented control strategies for achieving fine object manipulation first with the robotic hand of a space humanoid, Robonaut 2, and then with the ACT hand. I examined the unique control challenges, including uncontrollable joints and the requirement of accurate internal models, that arise due to the human hand's complex musculotendon structure and the potential advantages offered by the human hand's design, such as passive joint coupling to facilitate grasp shape adaptation and force production capabilities that are ideally suited for common manipulation tasks. Finally, inspired by the neuromuscular control strategies of the human hand, I have developed a novel hierarchical control strategy for the ACT hand and experimentally demonstrated improved grasp stability and manipulation capabilities compared to conventional robotic control laws. Through an in-depth exploration of human hand biomechanics and neuromuscular control, theoretical control analysis of robotic and human hands, and experimental demonstration of fine object manipulation, this work uncovers crucial insights into the sources of human hand dexterity that have the potential to drive innovative design and control strategies and bring robotic and prosthetic hands closer to human levels of dexterity.Mechanical Engineerin

Design and training of deep reinforcement learning agents

Deep reinforcement learning is a field of research at the intersection of reinforcement learning and deep learning. On one side, the problem that researchers address is the one of reinforcement learning: to act efficiently. A large number of algorithms were developed decades ago in this field to update value functions and policies, explore, and plan. On the other side, deep learning methods provide powerful function approximators to address the problem of representing functions such as policies, value functions, and models. The combination of ideas from these two fields offers exciting new perspectives. However, building successful deep reinforcement learning experiments is particularly difficult due to the large number of elements that must be combined and adjusted appropriately. This thesis proposes a broad overview of the organization of these elements around three main axes: agent design, environment design, and infrastructure design. Arguably, the success of deep reinforcement learning research is due to the tremendous amount of effort that went into each of them, both from a scientific and engineering perspective, and their diffusion via open source repositories. For each of these three axes, a dedicated part of the thesis describes a number of related works that were carried out during the doctoral research. The first part, devoted to the design of agents, presents two works. The first one addresses the problem of applying discrete action methods to large multidimensional action spaces. A general method called action branching is proposed, and its effectiveness is demonstrated with a novel agent, named BDQ, applied to discretized continuous action spaces. The second work deals with the problem of maximizing the utility of a single transition when learning to achieve a large number of goals. In particular, it focuses on learning to reach spatial locations in games and proposes a new method called Q-map to do so efficiently. An exploration mechanism based on this method is then used to demonstrate the effectiveness of goal-directed exploration. Elements of these works cover some of the main building blocks of agents: update methods, neural architectures, exploration strategies, replays, and hierarchy. The second part, devoted to the design of environments, also presents two works. The first one shows how various tasks and demonstrations can be combined to learn complex skill spaces that can then be reused to solve even more challenging tasks. The proposed method, called CoMic, extends previous work on motor primitives by using a single multi-clip motion capture tracking task in conjunction with complementary tasks targeting out-of-distribution movements. The second work addresses a particular type of control method vastly neglected in traditional environments but essential for animals: muscle control. An open source codebase called OstrichRL is proposed, containing a musculoskeletal model of an ostrich, an ensemble of tasks, and motion capture data. The results obtained by training a state-of-the-art agent on the proposed tasks show that controlling such a complex system is very difficult and illustrate the importance of using motion capture data. Elements of these works demonstrate the meticulous work that must go into designing environment parts such as: models, observations, rewards, terminations, resets, steps, and demonstrations. The third part, on the design of infrastructures, presents three works. The first one explains the difference between the types of time limits commonly used in reinforcement learning and why they are often treated inappropriately. In one case, tasks are time-limited by nature and a notion of time should be available to agents to maintain the Markov property of the underlying decision process. In the other case, tasks are not time-limited by nature, but time limits are used for convenience to diversify experiences. This is the most common case. It requires a distinction between time limits and environmental terminations, and bootstrapping should be performed at the end of partial episodes. The second work proposes to unify the most popular deep learning frameworks using a single library called Ivy, and provides new differentiable and framework-agnostic libraries built with it. Four such code bases are provided for gradient-based robot motion planning, mechanics, 3D vision, and differentiable continuous control environments. Finally, the third paper proposes a novel deep reinforcement learning library, called Tonic, built with simplicity and modularity in mind, to accelerate prototyping and evaluation. In particular, it contains implementations of several continuous control agents and a large-scale benchmark. Elements of these works illustrate the different components to consider when building the infrastructure for an experiment: deep learning framework, schedules, and distributed training. Added to these are the various ways to perform evaluations and analyze results for meaningful, interpretable, and reproducible deep reinforcement learning research.Open Acces

Generating whole body movements for dynamics anthropomorphic systems under constraints

Cette thèse étudie la question de la génération de mouvements corps-complet pour des systèmes anthropomorphes. Elle considère le problème de la modélisation et de la commande en abordant la question difficile de la génération de mouvements ressemblant à ceux de l'homme. En premier lieu, un modèle dynamique du robot humanoïde HRP-2 est élaboré à partir de l'algorithme récursif de Newton-Euler pour les vecteurs spatiaux. Un nouveau schéma de commande dynamique est ensuite développé, en utilisant une cascade de programmes quadratiques (QP) optimisant des fonctions coûts et calculant les couples de commande en satisfaisant des contraintes d'égalité et d'inégalité. La cascade de problèmes quadratiques est définie par une pile de tâches associée à un ordre de priorité. Nous proposons ensuite une formulation unifiée des contraintes de contacts planaires et nous montrons que la méthode proposée permet de prendre en compte plusieurs contacts non coplanaires et généralise la contrainte usuelle du ZMP dans le cas où seulement les pieds sont en contact avec le sol. Nous relions ensuite les algorithmes de génération de mouvement issus de la robotique aux outils de capture du mouvement humain en développant une méthode originale de génération de mouvement visant à imiter le mouvement humain. Cette méthode est basée sur le recalage des données capturées et l'édition du mouvement en utilisant le solveur hiérarchique précédemment introduit et la définition de tâches et de contraintes dynamiques. Cette méthode originale permet d'ajuster un mouvement humain capturé pour le reproduire fidèlement sur un humanoïde en respectant sa propre dynamique. Enfin, dans le but de simuler des mouvements qui ressemblent à ceux de l'homme, nous développons un modèle anthropomorphe ayant un nombre de degrés de liberté supérieur à celui du robot humanoïde HRP2. Le solveur générique est utilisé pour simuler le mouvement sur ce nouveau modèle. Une série de tâches est définie pour décrire un scénario joué par un humain. Nous montrons, par une simple analyse qualitative du mouvement, que la prise en compte du modèle dynamique permet d'accroitre naturellement le réalisme du mouvement.This thesis studies the question of whole body motion generation for anthropomorphic systems. Within this work, the problem of modeling and control is considered by addressing the difficult issue of generating human-like motion. First, a dynamic model of the humanoid robot HRP-2 is elaborated based on the recursive Newton-Euler algorithm for spatial vectors. A new dynamic control scheme is then developed adopting a cascade of quadratic programs (QP) optimizing the cost functions and computing the torque control while satisfying equality and inequality constraints. The cascade of the quadratic programs is defined by a stack of tasks associated to a priority order. Next, we propose a unified formulation of the planar contact constraints, and we demonstrate that the proposed method allows taking into account multiple non coplanar contacts and generalizes the common ZMP constraint when only the feet are in contact with the ground. Then, we link the algorithms of motion generation resulting from robotics to the human motion capture tools by developing an original method of motion generation aiming at the imitation of the human motion. This method is based on the reshaping of the captured data and the motion editing by using the hierarchical solver previously introduced and the definition of dynamic tasks and constraints. This original method allows adjusting a captured human motion in order to reliably reproduce it on a humanoid while respecting its own dynamics. Finally, in order to simulate movements resembling to those of humans, we develop an anthropomorphic model with higher number of degrees of freedom than the one of HRP-2. The generic solver is used to simulate motion on this new model. A sequence of tasks is defined to describe a scenario played by a human. By a simple qualitative analysis of motion, we demonstrate that taking into account the dynamics provides a natural way to generate human-like movements

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Locomoção bípede adaptativa a partir de uma única demonstração usando primitivas de movimento

Doutoramento em Engenharia EletrotécnicaEste trabalho aborda o problema de capacidade de imitação da locomoção humana através da utilização de trajetórias de baixo nível codificadas com primitivas de movimento e utilizá-las para depois generalizar para novas situações, partindo apenas de uma demonstração única. Assim, nesta linha de pensamento, os principais objetivos deste trabalho são dois: o primeiro é analisar, extrair e codificar demonstrações efetuadas por um humano, obtidas por um sistema de captura de movimento de forma a modelar tarefas de locomoção bípede. Contudo, esta transferência não está limitada à simples reprodução desses movimentos, requerendo uma evolução das capacidades para adaptação a novas situações, assim como lidar com perturbações inesperadas. Assim, o segundo objetivo é o desenvolvimento e avaliação de uma estrutura de controlo com capacidade de modelação das ações, de tal forma que a demonstração única apreendida possa ser modificada para o robô se adaptar a diversas situações, tendo em conta a sua dinâmica e o ambiente onde está inserido. A ideia por detrás desta abordagem é resolver o problema da generalização a partir de uma demonstração única, combinando para isso duas estruturas básicas. A primeira consiste num sistema gerador de padrões baseado em primitivas de movimento utilizando sistemas dinâmicos (DS). Esta abordagem de codificação de movimentos possui propriedades desejáveis que a torna ideal para geração de trajetórias, tais como a possibilidade de modificar determinados parâmetros em tempo real, tais como a amplitude ou a frequência do ciclo do movimento e robustez a pequenas perturbações. A segunda estrutura, que está embebida na anterior, é composta por um conjunto de osciladores acoplados em fase que organizam as ações de unidades funcionais de forma coordenada. Mudanças em determinadas condições, como o instante de contacto ou impactos com o solo, levam a modelos com múltiplas fases. Assim, em vez de forçar o movimento do robô a situações pré-determinadas de forma temporal, o gerador de padrões de movimento proposto explora a transição entre diferentes fases que surgem da interação do robô com o ambiente, despoletadas por eventos sensoriais. A abordagem proposta é testada numa estrutura de simulação dinâmica, sendo que várias experiências são efetuadas para avaliar os métodos e o desempenho dos mesmos.This work addresses the problem of learning to imitate human locomotion actions through low-level trajectories encoded with motion primitives and generalizing them to new situations from a single demonstration. In this line of thought, the main objectives of this work are twofold: The first is to analyze, extract and encode human demonstrations taken from motion capture data in order to model biped locomotion tasks. However, transferring motion skills from humans to robots is not limited to the simple reproduction, but requires the evaluation of their ability to adapt to new situations, as well as to deal with unexpected disturbances. Therefore, the second objective is to develop and evaluate a control framework for action shaping such that the single-demonstration can be modulated to varying situations, taking into account the dynamics of the robot and its environment. The idea behind the approach is to address the problem of generalization from a single-demonstration by combining two basic structures. The first structure is a pattern generator system consisting of movement primitives learned and modelled by dynamical systems (DS). This encoding approach possesses desirable properties that make them well-suited for trajectory generation, namely the possibility to change parameters online such as the amplitude and the frequency of the limit cycle and the intrinsic robustness against small perturbations. The second structure, which is embedded in the previous one, consists of coupled phase oscillators that organize actions into functional coordinated units. The changing contact conditions plus the associated impacts with the ground lead to models with multiple phases. Instead of forcing the robot’s motion into a predefined fixed timing, the proposed pattern generator explores transition between phases that emerge from the interaction of the robot system with the environment, triggered by sensor-driven events. The proposed approach is tested in a dynamics simulation framework and several experiments are conducted to validate the methods and to assess the performance of a humanoid robot

사람의 자연스러운 보행 동작 생성을 위한 물리 시뮬레이션 기반 휴머노이드 제어 방법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 8. 이제희.휴머노이드를 제어하여 사람의 자연스러운 이동 동작을 만들어내는 것은 컴퓨터그래픽스 및 로봇공학 분야에서 중요한 문제로 생각되어 왔다. 하지만, 이는 사람의 이동에서 구동기가 부족한 (underactuated) 특성과 사람의 몸의 복잡한 구조를 모방하고 시뮬레이션해야 한다는 점 때문에 매우 어려운 문제로 알려져왔다. 본 학위논문은 물리 시뮬레이션 기반 휴머노이드가 외부의 변화에 안정적으로 대응하고 실제 사람처럼 자연스럽고 다양한 이동 동작을 만들어내도록 하는 제어 방법을 제안한다. 우리는 실제 사람으로부터 얻을 수 있는 관찰 가능하고 측정 가능한 데이터를 최대한으로 활용하여 문제의 어려움을 극복했다. 우리의 접근 방법은 모션 캡처 시스템으로부터 획득한 사람의 모션 데이터를 활용하며, 실제 사람의 측정 가능한 물리적, 생리학적 특성을 복원하여 사용하는 것이다. 우리는 토크로 구동되는 이족 보행 모델이 다양한 스타일로 걸을 수 있도록 제어하는 데이터 기반 알고리즘을 제안한다. 우리의 알고리즘은 모션 캡처 데이터에 내재된 이동 동작 자체의 강건성을 활용하여 실제 사람과 같은 사실적인 이동 제어를 구현한다. 구체적으로는, 참조 모션 데이터를 재현하는 자연스러운 보행 시뮬레이션을 위한 관절 토크를 계산하게 된다. 알고리즘에서 가장 핵심적인 아이디어는 간단한 추종 제어기만으로도 참조 모션을 재현할 수 있도록 참조 모션을 연속적으로 조절하는 것이다. 우리의 방법은 모션 블렌딩, 모션 와핑, 모션 그래프와 같은 기존에 존재하는 데이터 기반 기법들을 이족 보행 제어에 활용할 수 있게 한다. 우리는 보다 사실적인 이동 동작을 생성하기 위해 사람의 몸을 세부적으로 모델링한, 근육에 의해 관절이 구동되는 인체 모델을 제어하는 이동 제어 시스템을 제안한다. 시뮬레이션에 사용되는 휴머노이드는 실제 사람의 몸에서 측정된 수치들에 기반하고 있으며 최대 120개의 근육을 가진다. 우리의 알고리즘은 최적의 근육 활성화 정도를 계산하여 시뮬레이션을 수행하며, 참조 모션을 충실히 재현하거나 혹은 새로운 상황에 맞게 모션을 적응시키기 위해 주어진 참조 모션을 수정하는 방식으로 동작한다. 우리의 확장가능한 알고리즘은 다양한 종류의 근골격 인체 모델을 최적의 근육 조합을 사용하며 균형을 유지하도록 제어할 수 있다. 우리는 다양한 스타일로 걷기 및 달리기, 모델의 변화 (근육의 약화, 경직, 관절의 탈구), 환경의 변화 (외력), 목적의 변화 (통증의 감소, 효율성의 최대화)에 대한 대응, 방향 전환, 회전, 인터랙티브하게 방향을 바꾸며 걷기 등과 같은 보다 난이도 높은 동작들로 이루어진 예제를 통해 우리의 접근 방법이 효율적임을 보였다.Controlling artificial humanoids to generate realistic human locomotion has been considered as an important problem in computer graphics and robotics. However, it has been known to be very difficult because of the underactuated characteristics of the locomotion dynamics and the complex human body structure to be imitated and simulated. In this thesis, we presents controllers for physically simulated humanoids that exhibit a rich set of human-like and resilient simulated locomotion. Our approach exploits observable and measurable data of a human to effectively overcome difficulties of the problem. More specifically, our approach utilizes observed human motion data collected by motion capture systems and reconstructs measured physical and physiological properties of a human body. We propose a data-driven algorithm to control torque-actuated biped models to walk in a wide range of locomotion skills. Our algorithm uses human motion capture data and realizes an human-like locomotion control facilitated by inherent robustness of the locomotion motion. Concretely, it takes reference motion and generates a set of joint torques to generate human-like walking simulation. The idea is continuously modulating the reference motion such that even a simple tracking controller can reproduce the reference motion. A number of existing data-driven techniques such as motion blending, motion warping, and motion graph can facilitate the biped control with this framework. We present a locomotion control system that controls detailed models of a human body with the musculotendon actuating process to create more human-like simulated locomotion. The simulated humanoids are based on measured properties of a human body and contain maximum 120 muscles. Our algorithm computes the optimal coordination of muscle activations and actively modulates the reference motion to fathifully reproduce the reference motion or adapt the motion to meet new conditions. Our scalable algorithm can control various types of musculoskeletal humanoids while seeking harmonious coordination of many muscles and maintaining balance. We demonstrate the strength of our approach with examples that allow simulated humanoids to walk and run in various styles, adapt to change of models (e.g., muscle weakness, tightness, joint dislocation), environments (e.g., external pushes), goals (e.g., pain reduction and efficiency maximization), and perform more challenging locomotion tasks such as turn, spin, and walking while steering its direction interactively.Contents Abstract i Contents iii List of Figures v 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.1 Computer Graphics Perspective . . . . . . . . . . . . . . . . . 3 1.1.2 Robotics Perspective . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.3 Biomechanics Perspective . . . . . . . . . . . . . . . . . . . . 7 1.2 Aim of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Previous Work 16 2.1 Biped Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.1 Controllers with Optimization . . . . . . . . . . . . . . . . . . 18 2.1.2 Controllers with Motion Capture Data . . . . . . . . . . . . . 20 2.2 Simulation of Musculoskeletal Humanoids . . . . . . . . . . . . . . . 21 2.2.1 Simulation of Specic Body Parts . . . . . . . . . . . . . . . . 21 2.2.2 Simulation of Full-Body Models . . . . . . . . . . . . . . . . . 22 2.2.3 Controllers for Musculoskeletal Humanoids . . . . . . . . . . . 23 3 Data-Driven Biped Control 24 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Data-Driven Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.2 Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4 Locomotion Control for Many-Muscle Humanoids 56 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2 Humanoid Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.2.1 Muscle Force Generation . . . . . . . . . . . . . . . . . . . . . 61 4.2.2 Muscle Force Transfer . . . . . . . . . . . . . . . . . . . . . . 64 4.2.3 Equation of Motion . . . . . . . . . . . . . . . . . . . . . . . . 66 4.3 Muscle Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.3.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.3.2 Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.3.3 Quadratic Programming Formulation . . . . . . . . . . . . . . 70 4.4 Trajectory Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5 Conclusion 84 A Mathematical Definitions 88 A.1 Definitions of Transition Function . . . . . . . . . . . . . . . . . . . . 88 B Humanoid Models 89 B.1 Torque-Actuated Biped Models . . . . . . . . . . . . . . . . . . . . . 89 B.2 Many-Muscle Humanoid Models . . . . . . . . . . . . . . . . . . . . . 91 C Dynamics of Musculotendon Actuators 94 C.1 Contraction Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 94 C.2 Initial Muscle States . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Glossary for Medical Terms 99 Bibliography 102 초록 113Docto

Acquisition and distribution of synergistic reactive control skills

Learning from demonstration is an afficient way to attain a new skill. In the context of autonomous robots, using a demonstration to teach a robot accelerates the robot learning process significantly. It helps to identify feasible solutions as starting points for future exploration or to avoid actions that lead to failure. But the acquisition of pertinent observationa is predicated on first segmenting the data into meaningful sequences. These segments form the basis for learning models capable of recognising future actions and reconstructing the motion to control a robot. Furthermore, learning algorithms for generative models are generally not tuned to produce stable trajectories and suffer from parameter redundancy for high degree of freedom robots This thesis addresses these issues by firstly investigating algorithms, based on dynamic programming and mixture models, for segmentation sensitivity and recognition accuracy on human motion capture data sets of repetitive and categorical motion classes. A stability analysis of the non-linear dynamical systems derived from the resultant mixture model representations aims to ensure that any trajectories converge to the intended target motion as observed in the demonstrations. Finally, these concepts are extended to humanoid robots by deploying a factor analyser for each mixture model component and coordinating the structure into a low dimensional representation of the demonstrated trajectories. This representation can be constructed as a correspondence map is learned between the demonstrator and robot for joint space actions. Applying these algorithms for demonstrating movement skills to robot is a further step towards autonomous incremental robot learning

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development