Stable whole-body motion generation for humanoid robots to imitate human motions

Abstract—This work presents a methodology to generate dynamically stable whole-body motions for a humanoid robot, which are converted from human motion capture data. The methodology consists of the kinematic and dynamical mappings for human-likeness and stability, respectively. The kinematic mapping includes the scaling of human foot and Zero Moment Point (ZMP) trajectories considering the geometric differences between a humanoid robot and a human. It also provides the conversion of human upper body motions using the method in [1]. The dynamic mapping modifies the humanoid pelvis motion to ensure the movement stability of humanoid wholebody motions, which are converted from the kinematic mapping. In addition, we propose a simplified human model to obtain a human ZMP trajectory, which is used as a reference ZMP trajectory for the humanoid robot to imitate during the kinematic mapping. A human whole-body dancing motion is converted by the methodology and performed by a humanoid robot with online balancing controllers. I

Walking trajectory generation & control of the humanoid robot: suralp

In recent years, the operational area of the robots started to extend and new functionalities are planned for them in our daily environments. As the human-robot interaction is being improved, the robots can provide support in elderly care, human assistance, rescue, hospital attendance and many other areas. With this motivation, an intensive research is focused around humanoid robotics in the last four decades. However, due to the nonlinear dynamics of the robot and high number of degrees of freedom, the robust balance of the bipedal walk is a challenging task. Smooth trajectory generation and online compensation methods are necessary to achieve a stable walk. In this thesis, Cartesian foot position references are generated as periodic functions with respect to a body-fixed coordinate frame. The online adjustment of these parameterized trajectories provides an opportunity in tuning the walking parameters without stopping the robot. The major contribution of this thesis in the context of trajectory generation is the smoothening of the foot trajectories and the introduction of ground push motion in the vertical direction. This pushing motion provided a dramatic improvement in the stability of the walking. Even though smooth foot reference trajectories are generated using the parameter based functions, the realization of a dynamically stable walk and maintenance of the robot balance requires walking control algorithms. This thesis introduces various control techniques to cope with disturbances or unevenness of the walking environment and compensate the mismatches between the planned and the actual walking based on sensory feedback. Moreover, an automatic homing procedure is proposed for the adjustment of the initial posture before the walking experiments. The presented control algorithms include ZMP regulation, foot orientation control, trunk orientation control, foot pitch torque difference compensation, body pitch angle correction, ground impact compensation and early landing modification. The effectiveness of the proposed trajectory generation and walking control algorithms is tested on the humanoid robot SURALP and a stable walk is achieved

Modification of Gesture-Determined-Dynamic Function with Consideration of Margins for Motion Planning of Humanoid Robots

The gesture-determined-dynamic function (GDDF) offers an effective way to handle the control problems of humanoid robots. Specifically, GDDF is utilized to constrain the movements of dual arms of humanoid robots and steer specific gestures to conduct demanding tasks under certain conditions. However, there is still a deficiency in this scheme. Through experiments, we found that the joints of the dual arms, which can be regarded as the redundant manipulators, could exceed their limits slightly at the joint angle level. The performance straightly depends on the parameters designed beforehand for the GDDF, which causes a lack of adaptability to the practical applications of this method. In this paper, a modified scheme of GDDF with consideration of margins (MGDDF) is proposed. This MGDDF scheme is based on quadratic programming (QP) framework, which is widely applied to solving the redundancy resolution problems of robot arms. Moreover, three margins are introduced in the proposed MGDDF scheme to avoid joint limits. With consideration of these margins, the joints of manipulators of the humanoid robots will not exceed their limits, and the potential damages which might be caused by exceeding limits will be completely avoided. Computer simulations conducted on MATLAB further verify the feasibility and superiority of the proposed MGDDF scheme

Bipedal humanoid robot control by fuzzy adjustment of the reference walking plane

The two-legged humanoid structure has advantages for an assistive robot in the human living and working environment. A bipedal humanoid robot can avoid typical obstacles at homes and offices, reach consoles and appliances designed for human use and can be carried in human transport vehicles. Also, it is speculated that the absorption of robots in the human shape into the human society can be easier than that of other artificial forms. However, the control of bipedal walk is a challenge. Walking performance on solely even floor is not satisfactory. The complications of obtaining a balanced walk are dramatically more pronounced on uneven surfaces like inclined planes, which are quite commonly encountered in human surroundings. The difficulties lie in a variety of tasks ranging from sensor and data fusion to the design of adaptation systems which respond to changing surface conditions. This thesis presents a study on bipedal walk on inclined planes with changing slopes. A Zero Moment Point (ZMP) based gait synthesis technique is employed. The pitch angle reference for the foot sole plane −as expressed in a coordinate frame attached at the robot body − is adjusted online by a fuzzy logic system to adapt to different walking surface slopes. Average ankle pitch torques and the average value of the body pitch angle, computed over a history of a predetermined number of sampling instants, are used as the inputs to this system. The proposed control method is tested via walking experiments with the 29 degreesof- freedom (DOF) human-sized full-body humanoid robot SURALP (Sabanci University Robotics Research Laboratory Platform). Experiments are performed on even floor and inclined planes with different slopes. The results indicate that the approach presented is successful in enabling the robot to stably enter, ascend and leave inclined planes with 15 percent (8.5 degrees) grade. The thesis starts with a terminology section on bipedal walking and introduces a number of successful humanoid robot projects. A survey of control techniques for the walk on uneven surfaces is presented. The design and construction of the experimental robotic platform SURALP is discussed with the mechanical, electronic, walking reference generation and control aspects. The fuzzy reference adjustment system proposed for the walk on inclined planes is detailed and experimental results are presented

Generation of whole-body motion for humanoid robots with the complete dynamics

Cette thèse propose une solution au problème de la génération de mouvements pour les robots humanoïdes. Le cadre qui est proposé dans cette thèse génère des mouvements corps-complet en utilisant la dynamique inverse avec l'espace des tâches et en satisfaisant toutes les contraintes de contact. La spécification des mouvements se fait à travers objectifs dans l'espace des tâches et la grande redondance du système est gérée avec une pile de tâches où les tâches moins prioritaires sont atteintes seulement si elles n'interfèrent pas avec celles de plus haute priorité. À cette fin, un QP hiérarchique est utilisé, avec l'avantage d'être en mesure de préciser tâches d'égalité ou d'inégalité à tous les niveaux de la hiérarchie. La capacité de traiter plusieurs contacts non-coplanaires est montrée par des mouvements où le robot s'assoit sur une chaise et monte une échelle. Le cadre générique de génération de mouvements est ensuite appliqué à des études de cas à l'aide de HRP-2 et Romeo. Les mouvements complexes et similaires à l'humain sont obtenus en utilisant l'imitation du mouvement humain où le mouvement acquis passe par un processus cinématique et dynamique. Pour faire face à la nature instantanée de la dynamique inverse, un générateur de cycle de marche est utilisé comme entrée pour la pile de tâches qui effectue une correction locale de la position des pieds sur la base des points de contact permettant de marcher sur un terrain accidenté. La vision stéréo est également introduite pour aider dans le processus de marche. Pour une récupération rapide d'équilibre, le capture point est utilisé comme une tâche contrôlée dans une région désirée de l'espace. En outre, la génération de mouvements est présentée pour CHIMP, qui a besoin d'un traitement particulier.This thesis aims at providing a solution to the problem of motion generation for humanoid robots. The proposed framework generates whole-body motion using the complete robot dynamics in the task space satisfying contact constraints. This approach is known as operational-space inverse-dynamics control. The specification of the movements is done through objectives in the task space, and the high redundancy of the system is handled with a prioritized stack of tasks where lower priority tasks are only achieved if they do not interfere with higher priority ones. To this end, a hierarchical quadratic program is used, with the advantage of being able to specify tasks as equalities or inequalities at any level of the hierarchy. Motions where the robot sits down in an armchair and climbs a ladder show the capability to handle multiple non-coplanar contacts. The generic motion generation framework is then applied to some case studies using HRP-2 and Romeo. Complex and human-like movements are achieved using human motion imitation where the acquired motion passes through a kinematic and then dynamic retargeting processes. To deal with the instantaneous nature of inverse dynamics, a walking pattern generator is used as an input for the stack of tasks which makes a local correction of the feet position based on the contact points allowing to walk on non-planar surfaces. Visual feedback is also introduced to aid in the walking process. Alternatively, for a fast balance recovery, the capture point is introduced in the framework as a task and it is controlled within a desired region of space. Also, motion generation is presented for CHIMP which is a robot that needs a particular treatment

Motion Recognition and Planning Using Gaussian Process Dynamical Models

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 박종우.본 논문에서는 로봇이 해석적으로 정의되지 않은 환경에 대응하는 문제에 관해 다룬다. 이 환경에는 로봇이 피해야 하는 장애물과 하지 외골격 로봇 착용자의 동작 의도와 밀접하게 연관된 지형지물이 있다. 관절 공간과 그 저차원 공간에서의 경로 계획법을 통해 장애물을 회피 하였고 기계학습 기법을 통해 지형지물에 기인한 사람의 동작 의도를 추정하였다. 먼저 Gaussian process dynamical models (GPDM) 기반으로 하지 외골격 로봇 착용자의 운동 의도를 추정하는 알고리즘을 제안하였다. 관측한 짧은 시계열 입력 값에 대하여 이에 상응하는 저차원 공간 좌표를 Gaussian process regression 을 통해 얻는다. 각 모델에 대한 유사도는 학습 데이터에 대한 관측 값과 그 저차원 공간 좌표의 로그 조건부 확률분포 형태로 표현된다. 이 유사도를 비교하여 가장 가능성 있는 동작을 추정한다. 하지 외골격 로봇 프로토타입 및 동작 추적 시스템을 이용한 물리적 실험을 통해 우리의 알고리즘을 검증하였다. 다음으로는 적응적으로 스텝사이즈를 결정하는 RRT 알고리즘을 제안하였다. 지수 곱(Product of Exponentials, PoE) 형태로 표현된 로봇의 정기구학과 표준 작용소 노름 부등식으로부터 직렬 개 연쇄 로봇의 엔드이펙터의 작업공간에서의 최대 변위와 관절 공간에서의 변위에 대한 부등식으로 유도하였다. 이 부등식을 이용하여 주어진 장애물의 최소 크기에 대하여 적응적으로 스텝사이즈를 결정하였다. 10 자유도 평면 개 연쇄 로봇과 7축 산업용 매니퓰레이터를 이용하여 우리의 알고리즘을 검증하였다. 마지막으로 사람의 시연 동작을 GPDM을 이용해 저차원 공간으로 학습하여, 사람과 유사한 동작을 생성하는 저차원 공간에서의 경로 계획 방법을 제안하였다. 앞서 유도한 부등식을 저차원 공간에서의 변위와 작업공간에서의 각 링크의 변위에 대한 부등식으로 확장하였다. 이를 이용하여 작업공간에서 정의된 장애물을 샘플링 기반으로 저차원 공간으로 매핑하였다. 그리고 학습한 동작과 새롭게 생성한 동작 사이의 유사성을 측정하는 측도를 GPDM 커널함수를 기반으로 정의하였다. 시뮬레이터와 실제 로봇에 적용해 봄으로써 제안한 방법의 유효성을 검증하였다.In this thesis, we deal with the problems that the robot copes with unstructured environments. Examples of such environments are obstacles that robots should avoid and terrain features that are closely related to the intentions of the wearer of an exoskeleton robot. We make robots to avoid obstacles through path planning algorithms in joint space and its low-dimensional space. We also estimate human motion intentions caused by terrain features using machine learning techniques. First, we propose an algorithm based on Gaussian process dynamical models (GPDM) to estimate motion intention of the wearer of exoskeleton robot. For the observed short time series input values, the corresponding low dimensional space coordinates are obtained via Gaussian process regression. The similarity for each model is expressed in the form of the logarithm of the conditional probability distribution of observed values and its low-dimensional coordinates given the training data. This similarity is compared to estimate the most likely motion. We validate our algorithm through physical experiments using an exoskeleton robot prototype and motion tracking system. Next, we propose a rapidly-exploring random tree (RRT) algorithm that adaptively determines an appropriate stepsize. Using a standard operator norm inequality and the forward kinematics equations expressed as the product of exponentials, we derive an approximate bound on the Cartesian displacement of the open chain tip for a given joint space displacement. Using this inequality bound, we adaptively determine the stepsize for a given minimum obstacle size. We verify our algorithm by numerical experiments using a ten-dof planar open chain robot and a seven-axis industrial manipulator. Finally, we propose a path planning method in a low-dimensional space that generates a human-like motion by learning the human demonstration motion using GPDM. We extend the above inequality to the inequality between displacement in the low-dimensional space and displacement of each links in the workspace. We use this to map the obstacles defined in the workspace to the low-dimensional space based on the uniform sampling. In addition, we define a measure based on the GPDM kernel function to measure the similarity between the learned motion and the newly generated motion. We validate the proposed method by applying it to a simulator and an actual robot.Abstract iii List of Tables xi List of Figures xiii 1 Introduction 1 1.1 Contributions of This Thesis . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 GPDM-Based Human Motion Intention Recognition for Lower-Limb Exoskeleton . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 An Adaptive Stepsize RRT Planning Algorithm for Open Chain Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.3 A Gaussian Process Dynamical Model-Based Planning Method 8 1.2 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Preliminaries 11 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Gaussian Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.1 Gaussian Process Regression . . . . . . . . . . . . . . . . . . 12 2.2.2 Gaussian Process Latent Variable models . . . . . . . . . . . 16 2.2.3 Gaussian Process Dynamical Models . . . . . . . . . . . . . . 19 2.3 Forward Kinematics of Open Chains . . . . . . . . . . . . . . . . . . 23 3 GPDM-Based Human Motion Intention Recognition for Lower-Limb Exoskeleton 27 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Human Motion Intention Recognition using GPDM . . . . . . . . . 29 3.3 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3.1 Human Motion Capture Data . . . . . . . . . . . . . . . . . 30 3.3.2 Sensor Mock-up Data . . . . . . . . . . . . . . . . . . . . . . 33 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.4.1 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . 35 3.4.2 Human Motion Capture Data . . . . . . . . . . . . . . . . . 40 3.4.3 Sensor Mock-up Data . . . . . . . . . . . . . . . . . . . . . . 44 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.5.1 Comparison both Data Sets . . . . . . . . . . . . . . . . . . . 45 3.5.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . 49 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4 An Adaptive Stepsize RRT Planning Algorithm for Open Chain Robots 53 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2 A Cartesian Displacement Bound for Open Chains . . . . . . . . . 54 4.3 Adaptive Stepsize RRT . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.4.1 Ten-Dof Planar Robot . . . . . . . . . . . . . . . . . . . . . . 65 4.4.2 Ten-Dof Planar Robot Case II: Latent Space RRT . . . . . 70 4.4.3 Seven-DoF Industrial Robot Arm . . . . . . . . . . . . . . . 77 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5 A Gaussian Process Dynamical Model-Based Planning Method 85 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.2 Learning from Demonstration Framework . . . . . . . . . . . . . . . 88 5.2.1 Learning a New Pose in the Latent Space . . . . . . . . . . 90 5.2.2 Constraints in Latent Space . . . . . . . . . . . . . . . . . . 91 5.2.3 Mapping Obstacle into Latent Space . . . . . . . . . . . . . 92 5.2.4 Motion Planning in Latent Space . . . . . . . . . . . . . . . 102 5.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 5.3.1 Grasping Experiments . . . . . . . . . . . . . . . . . . . . . . 108 5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 6 Conclusion 117 Bibliography 121 Abstract 128Docto

Humanizing robot dance movements

Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 201

Design, modelling and control of a biped robot platform based on Poppy project

Taking as a reference the open source 3D printed robot called ''Poppy'' (https://www.poppy-project.org/) this project aims to develop a new biped robot platform using standard size servomotors. To accomplish this project is required to design a new structure to be able to host the chosen motors and also add a new degree of freedom for each leg, in order to obtain a 12 DoF robot. The next objetive is the modelling and simulation. For that purpose Gazebo simulator will be used to provide the option to be controlled with ROS. In addition various sensors will be added to the model, in order to obtain the feedback necessary for the control algorithm, which is the last objetive of this project