Sistema de aquisição de dados por interface háptica

Mestrado em Engenharia MecânicaNeste trabalho e apresentada uma interface háptica com realimentação de força para a teleoperação de um robô humanoide é que aborda um novo conceito destinado à aprendizagem por demonstração em robôs, denominado de ensino telecinestésico. A interface desenvolvida pretende promover o ensino cinestésico num ambiente de tele-robótica enriquecido pela virtualização háptica do ambiente e restrições do robô. Os dados recolhidos através desta poderão então ser usados em aprendizagem por demonstração, uma abordagem poderosa que permite aprender padrões de movimento sem a necessidade de modelos dinâmicos complexos, mas que geralmente é apresentada com demonstrações que não são fornecidas teleoperando os robôs. Várias experiências são referidas onde o ensino cinestésico em aprendizagem robótica foi utilizado com um sucesso considerável, bem como novas metodologias e aplicações com aparelhos hápticos. Este trabalho foi realizado com base na plataforma proprietária de 27 graus-de-liberdade do Projeto Humanoide da Universidade de Aveiro (PHUA), definindo novas methodologias de comando em tele-operação, uma nova abordagem de software e ainda algumas alterações ao hardware. Um simulador de corpo inteiro do robô em MATLAB SimMechanics é apresentado que é capaz de determinar os requisitos dinâmicos de binário de cada junta para uma dada postura ou movimento, exemplificando com um movimento efectuado para subir um degrau. Ir a mostrar algumas das potencialidades mas também algumas das limitações restritivas do software. Para testar esta nova abordagem tele-cinestésica são dados exemplos onde o utilizador pode desenvolver demonstrações interagindo fisicamente com o robô humanoide através de um joystick háptico PHANToM. Esta metodologia ir a mostrar que permite uma interação natural para o ensino e perceção tele-robóticos, onde o utilizador fornece instruções e correções funcionais estando ciente da dinâmica do sistema e das suas capacidades e limitações físicas. Ser a mostrado que a abordagem consegue atingir um bom desempenho mesmo com operadores inexperientes ou não familiarizados com o sistema. Durante a interação háptica, a informação sensorial e as ordens que guiam a uma tarefa específica podem ser gravados e posteriormente utilizados para efeitos de aprendizagem.In this work an haptic interface using force feedback for the teleoperation of a humanoid robot is presented, that approaches a new concept for robot learning by demonstration known as tele-kinesthethic teaching. This interface aims at promoting kinesthethic teaching in telerobotic environments enriched by the haptic virtualization of the robot's environment and restrictions. The data collected through this interface can later be in robot learning by demonstration, a powerful approach for learning motion patterns without complex dynamical models, but which is usually presented using demonstrations that are not provided by teleoperating the robots. Several experiments are referred where kinesthetic teaching for robot learning was used with considerable success, as well as other new methodologies and applications with haptic devices. This work was conducted on the proprietary 27 DOF University of Aveiro Humanoid Project (PHUA) robot, de ning new wiring and software solutions, as well as a new teleoperation command methodology. A MATLAB Sim- Mechanics full body robot simulator is presented that is able to determine dynamic joint torque requirements for a given robot movement or posture, exempli ed with a step climbing application. It will show some of the potentialities but also some restricting limitations of the software. To test this new tele-kinesthetic approach, examples are shown where the user can provide demonstrations by physically interacting with the humanoid robot through a PHANToM haptic joystick. This methodology will show that it enables a natural interface for telerobotic teaching and sensing, in which the user provides functional guidance and corrections while being aware of the dynamics of the system and its physical capabilities and / or constraints. It will also be shown that the approach can have a good performance even with inexperienced or unfamiliarized operators. During haptic interaction, the sensory information and the commands guiding the execution of a speci c task can be recorded and that data log from the human-robot interaction can be later used for learning purposes

Bipedal Walking Energy Minimization by Reinforcement Learning with Evolving Policy Parameterization

We present a learning-based approach for minimizing the electric energy consumption during walking of a passively-compliant bipedal robot. The energy consumption is reduced by learning a varying-height center-of-mass trajectory which uses efficiently the robots passive compliance. To do this, we propose a reinforcement learning method which evolves the policy parameterization dynamically during the learning process and thus manages to find better policies faster than by using fixed parameterization. The method is first tested on a function approximation task, and then applied to the humanoid robot COMAN where it achieves significant energy reduction. © 2011 IEEE

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

Anticipatory models of human movements and dynamics: the roadmap of the AnDy project

International audienceFuture robots will need more and more anticipation capabilities, to properly react to human actions and provide efficient collaboration. To achieve this goal, we need new technologies that not only estimate the motion of the humans, but that fully describe the whole-body dynamics of the interaction and that can also predict its outcome. These hardware and software technologies are the goal of the European project AnDy. In this paper, we describe the roadmap of AnDy, which leverages existing technologies to endow robots with the ability to control physical collaboration through intentional interaction. To achieve this goal, AnDy relies on three technological and scientific breakthroughs. First, AnDy will innovate the way of measuring human whole-body motions by developing the wearable AnDySuit, which tracks motions and records forces. Second, AnDy will develop the AnDyModel, which combines ergonomic models with cognitive predictive models of human dynamic behavior in collaborative tasks, learned from data acquired with the AnDySuit. Third, AnDy will propose AnDyControl, an innovative technology for assisting humans through pre-dictive physical control, based on AnDyModel. By measuring and modeling human whole-body dynamics, AnDy will provide robots with a new level of awareness about human intentions and ergonomy. By incorporating this awareness on-line in the robot's controllers, AnDy paves the way for novel applications of physical human-robot collaboration in manufacturing, health-care, and assisted living

Geometry-aware Manipulability Learning, Tracking and Transfer

Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or apply a specific force. In this context, this paper presents a novel \emph{manipulability transfer} framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.Comment: Accepted for publication in the Intl. Journal of Robotics Research (IJRR). Website: https://sites.google.com/view/manipulability. Code: https://github.com/NoemieJaquier/Manipulability. 24 pages, 20 figures, 3 tables, 4 appendice

Passivity-based variable impedance control for redundant manipulators

Kinematic redundancy significantly improves the dexterity and flexibility of robotic manipulators. The redundant degrees of freedom can be exploited to fulfill additional tasks that can be executed without disturbing the primary task. In this work, we investigate how a time varying impedance behavior can be embedded into redundant manipulators where it is desired to achieve such a behavior both for the primary and null space tasks. A passivity based controller is developed, relying on the concept of energy tanks which are filled by the dissipated power in the system, and compensate for non-passive control actions. This guarantees that the system remains passive, which ensures stable interactions with any passive environment. The method is validated in simulations where the interactive behavior of the main and null space tasks is specified by a time varying stiffness profile

외란 및 토크 대역폭 제한을 고려한 토크 기반의 작업 공간 제어

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2021.8. 박재흥.The thesis aims to improve the control performance of the torque-based operational space controller under disturbance and torque bandwidth limitation. Torque-based robot controllers command the desired torque as an input signal to the actuator. Since the torque is at force-level, the torque-controlled robot is more compliant to external forces from the environment or people than the position-controlled robot. Therefore, it can be used effectively for the tasks involving contact such as legged locomotion or human-robot interaction. Operational space control strengthens this advantage for redundant robots due to the inherent compliance in the null space of given tasks. However, high-level torque-based controllers have not been widely used for transitional robots such as industrial manipulators due to the low performance of precise control. One of the reasons is the uncertainty or disturbance in the kinematic and dynamic properties of the robot model. It leads to the inaccurate computation of the desired torque, deteriorating the control stability and performance. To estimate and compensate the disturbance using only proprioceptive sensors, the disturbance observer has been developed using inverse dynamics. It requires the joint acceleration information, which is noisy due to the numerical error in the second-order derivative of the joint position. In this work, a contact-consistent disturbance observer for a floating-base robot is proposed. The method uses the fixed contact position of the supporting foot as the kinematic constraints to estimate the joint acceleration error. It is incorporated into the dynamics model to reduce its effect on the disturbance torque solution, by which the observer becomes less dependent on the low-pass filter design. Another reason for the low performance of precise control is torque bandwidth limitation. Torque bandwidth is determined by the relationship between the input torque commanded to the actuator and the torque actually transmitted into the link. It can be regulated by various factors such as inner torque feedback loop, actuator dynamics, and joint elasticity, which deteriorates the control stability and performance. Operational space control is especially prone to this problem, since the limited bandwidth of a single actuator can reduce the performance of all related tasks simultaneously. In this work, an intuitive way to penalize low performance actuators is proposed for the operational space controller. The basic concept is to add joint torques only to high performance actuators recursively, which has the physical meaning of the joint-weighted torque solution considering each actuator performance. By penalizing the low performance actuators, the torque transmission error is reduced and the task performance is significantly improved. In addition, the joint trajectory is not required, which allows compliance in redundancy. The results of the thesis were verified by experiments using the 12-DOF biped robot DYROS-RED and the 7-DOF robot manipulator Franka Emika Panda.본 학위 논문은 외란과 토크 대역폭 제한이 존재할 때 토크 기반 작업 공간 제어기의 제어 성능을 높이는 것을 목표로 한다. 토크 기반의 로봇 제어기는 목표 토크를 입력 신호로서 구동기에 전달한다. 토크는 힘 레벨이기 때문에, 토크 제어 로봇은 위치 제어 로봇에 비해 외부 환경이나 사람으로부터 가해지는 외력에 더 유연하게 대응할 수 있다. 그러므로 토크 제어는 보행이나 인간-로봇 상호작용과 같은 접촉을 포함하는 작업을 위해 효과적으로 사용될 수 있다. 작업 공간 제어는 이러한 토크 제어의 장점을 더 강화시킬 수 있는데, 로봇이 여유 자유도가 있을 때 작업의 영공간에서 존재하는 모션들이 내재적으로 유연하기 때문이다. 그러나 이러한 장점에도 불구하고 토크 기반의 로봇 제어기는 정밀 제어 성능이 떨어지기 때문에 산업용 로봇 팔과 같은 전통적인 로봇에는 널리 사용되지 못했다. 그 이유 중 한 가지는 로봇 모델의 기구학 및 동역학 물성치에 존재하는 외란이다. 모델 오차는 목표 토크를 계산할 때 오차를 유발하며, 이것이 제어 안정성과 성능을 약화시키게 된다. 외란을 내재 센서만을 이용하여 추정 및 보상하기 위해 역동역학 기반의 외란 관측기가 개발되어 왔다. 외란 관측기는 역동역학 계산을 위해 관절 각가속도 정보가 필요한데, 이 값이 관절 위치를 두 번 미분한 값이기 때문에 수치적인 오차로 노이즈해지는 문제가 있었다. 본 연구에서는 부유형 기저 로봇을 위한 접촉 조건이 고려된 외란 관측기가 제안되었다. 제안된 방법은 로봇의 고정된 접촉 지점에 대한 기구학적인 구속 조건을 이용하여 관절 각가속도 오차를 추정한다. 추정된 오차를 동역학 모델에 반영하여 외란 토크를 계산함으로써 저역 통과 필터 성능에 대한 의존도를 줄일 수 있다. 토크 기반 제어의 정밀 제어 성능이 떨어지는 또 다른 이유 중 하나는 토크 대역폭 제한이다. 토크 대역폭은 구동기에 전달되는 입력 토크와 실제 링크에 전달되는 토크와의 관계로 결정된다. 토크 대역폭은 구동기 내부의 토크 피드백 루프, 구동기 동역학, 관절 탄성 등의 요인들에 의해 제한될 수 있는데 이것이 제어 안정성 및 성능을 감소시킨다. 작업 공간 제어는 특히 이 문제에 취약한데, 대역폭이 제한된 구동기 하나가 그와 연관된 모든 작업 공간의 제어 성능을 감소시킬 수 있기 때문이다. 본 연구에서는 작업 공간 제어기에서 성능이 낮은 구동기의 사용을 제한하기 위한 직관적인 전략이 제안되었다. 기본 컨셉은 작업 제어를 위한 토크 솔루션에 성능이 좋은 관절에만 추가적으로 토크 솔루션을 더해나가는 것으로, 이것은 각 관절의 가중치가 고려된 토크 솔루션이 되는 것을 의미한다. 성능이 낮은 구동기의 사용을 제한함으로써 토크 전달 오차가 줄어들고 작업 성능이 크게 향상될 수 있다. 본 학위 논문의 연구 결과들은 12자유도 이족 보행 로봇 DYROS-RED와 7자유도 로봇 팔 Franka Emika Panda를 이용한 실험을 통해 검증되었다.1 INTRODUCTION 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contributions of Thesis . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Overview of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 BACKGROUNDS 6 2.1 Operational Space Control . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Dynamics Formulation . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Fixed-Base Dynamics . . . . . . . . . . . . . . . . . . . . 9 2.2.1.1 Joint Space Formulation . . . . . . . . . . . . . 9 2.2.1.2 Operational Space Formulation . . . . . . . . . . 11 2.2.2 Floating-Base Dynamics . . . . . . . . . . . . . . . . . . . 12 2.2.2.1 Joint Space Formulation . . . . . . . . . . . . . 12 2.2.2.2 Operational Space Formulation . . . . . . . . . . 14 2.3 Position Tracking via PD Control . . . . . . . . . . . . . . . . . . 17 2.3.1 Torque Solution . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.2 Orientation Control . . . . . . . . . . . . . . . . . . . . . 19 3 CONTACT-CONSISTENT DISTURBANCE OBSERVER FOR FLOATING-BASE ROBOTS 22 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 Momentum-Based Disturbance Observer . . . . . . . . . . . . . . 24 3.3 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 External Force Estimation . . . . . . . . . . . . . . . . . . 33 3.4.3 Internal Disturbance Rejection . . . . . . . . . . . . . . . 35 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 OPERATIONAL SPACE CONTROL UNDER ACTUATOR BANDWIDTH LIMITATION 40 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1 General Concepts . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.2 OSF-Based Torque Solution . . . . . . . . . . . . . . . . . 45 4.2.3 Comparison With a Typical Method . . . . . . . . . . . . 47 4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.4 Comparison With Other Approaches . . . . . . . . . . . . . . . . 61 4.4.1 Controller Formulation . . . . . . . . . . . . . . . . . . . . 62 4.4.1.1 The Proposed Method . . . . . . . . . . . . . . . 62 4.4.1.2 The OSF Controller . . . . . . . . . . . . . . . . 62 4.4.1.3 The OSF-Filter Controller . . . . . . . . . . . . 62 4.4.1.4 The OSF-Joint Controller . . . . . . . . . . . . . 67 4.4.1.5 The Joint Controller . . . . . . . . . . . . . . . . 68 4.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.5 Frequency Response of Joint Torque . . . . . . . . . . . . . . . . 72 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5 CONCLUSION 85 Abstract (In Korean) 100박

Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation

Queißer J. Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation. Bielefeld: Universität Bielefeld; 2018.Modern robotic applications pose complex requirements with respect to the adaptation of actions regarding the variability in a given task. Reinforcement learning can optimize for changing conditions, but relearning from scratch is hardly feasible due to the high number of required rollouts. This work proposes a parameterized skill that generalizes to new actions for changing task parameters. The actions are encoded by a meta-learner that provides parameters for task-specific dynamic motion primitives. Experimental evaluation shows that the utilization of parameterized skills for initialization of the optimization process leads to a more effective incremental task learning. A proposed hybrid optimization method combines a fast coarse optimization on a manifold of policy parameters with a fine-grained parameter search in the unrestricted space of actions. It is shown that the developed algorithm reduces the number of required rollouts for adaptation to new task conditions. Further, this work presents a transfer learning approach for adaptation of learned skills to new situations. Application in illustrative toy scenarios, for a 10-DOF planar arm, a humanoid robot point reaching task and parameterized drumming on a pneumatic robot validate the approach. But parameterized skills that are applied on complex robotic systems pose further challenges: the dynamics of the robot and the interaction with the environment introduce model inaccuracies. In particular, high-level skill acquisition on highly compliant robotic systems such as pneumatically driven or soft actuators is hardly feasible. Since learning of the complete dynamics model is not feasible due to the high complexity, this thesis examines two alternative approaches: First, an improvement of the low-level control based on an equilibrium model of the robot. Utilization of an equilibrium model reduces the learning complexity and this thesis evaluates its applicability for control of pneumatic and industrial light-weight robots. Second, an extension of parameterized skills to generalize for forward signals of action primitives that result in an enhanced control quality of complex robotic systems. This thesis argues for a shift in the complexity of learning the full dynamics of the robot to a lower dimensional task-related learning problem. Due to the generalization in relation to the task variability, online learning for complex robots as well as complex scenarios becomes feasible. An experimental evaluation investigates the generalization capabilities of the proposed online learning system for robot motion generation. Evaluation is performed through simulation of a compliant 2-DOF arm and scalability to a complex robotic system is demonstrated for a pneumatically driven humanoid robot with 8-DOF