Balancing of humanoid robot using contact force/moment control by task-oriented whole body control framework

Balancing control of humanoid robots is of great importance since it is a necessary functionality not only for maintaining a certain position without falling, but also for walking and running. For position controlled robots, the for-ce/torque sensors at each foot are utilized to measure the contact forces and moments, and these values are used to compute the joint angles to be commanded for balancing. The proposed approach in this paper is to maintain balance of torque-controlled robots by controlling contact force and moment using whole-body control framework with hierarchical structure. The control of contact force and moment is achieved by exploiting the full dynamics of the robot and the null-space motion in this control framework. This control approach enables compliant balancing behavior. In addition, in the case of double support phase, required contact force and moment are controlled using the redundancy in the contact force and moment space. These algorithms are implemented on a humanoid legged robot and the experimental results demonstrate the effectiveness of them.     

Constructive understanding and reproduction of functions of gluteus medius by using a musculoskeletal walking robot

Several factors affect the performance of humanoid walking. One factor is the complex nature of lower limbs, especially the muscles around the pelvis that contribute significantly to the stability and adaptivity of humanoid locomotion. The significance of this muscle group assures a impact on the facility of walking robots once the nature of its contribution is understood, and it can be replicated on robots. To propose a mechanical structure that facilitates walking in robots, we realized a muscle by modeling its pelvis region like that of a humanoid and developing a musculoskeletal humanoid robot. Especially, we focused on the gluteus medius, which is important for the general stability against frontal movements of the hip. Furthermore, it passively changes its influence on such motions; this is helpful during the different phases of locomotion. These changes depend on the alignment of the pelvis and femur. We confirmed the viability of the robotic gluteus medius, which was simplified to a model of two partial muscles by accomplishing the walking using this robot. This accomplishment verifies our hypothesis that using this model, the supporting functionality for the locomotion of the muscle can be reproduced and enhances the biological plausibility.     

A Biologically Inspired Biped Locomotion Strategy for Humanoid Robots: Modulation of Sinusoidal Patterns by a Coupled Oscillator Model

Biological systems seem to have a simpler but more robust locomotion strategy than that of the existing biped walking controllers for humanoid robots. We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center-of-pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We do not explicitly use dynamical parameters of the humanoid robot. We hypothesize that a similar mechanism may exist in biological systems. We applied the proposed biologically inspired control strategy to our newly developed human-sized humanoid robot computational brain (CB) and a small size humanoid robot, enabling them to generate successful stepping and walking patterns.     

Development of a leg part of a humanoid robot-design of a biped walking robot having antagonistic driven joints using a nonlinear spring mechanism

The authors are engaged in studies of biped walking robots from the following two viewpoints. One is a viewpoint as a human science. The other is a viewpoint towards the development of humanoid robots. In the current research concerning a biped walking robot, there is no developed example of a life-size biped walking robot with antagonistically driven joints by which the human musculo-skeletal system is imitated in the lower limbs. Humans are considered to exhibit walking behavior which is both efficient and capable of flexibly coping with contact with the outside environment. However, developed biped walking robots cannot realize human walking. The human joint is driven by two or more antagonistic muscle groups. Humans can vary the joint stiffness, using nonlinear spring characteristics possessed by the muscles themselves. The function is an indispensable function for a humanoid. Therefore, the authors designed and built an anthropomorphic biped walking robot having antagonistic driven joints. In this paper, the authors introduce the design method of the robot. The authors performed walking experiments with the robot. As a result, a quasi-dynamic biped walking using antagonist driven joint was realized. The walking speed was 7.68 s per step with a 0.1 m step length.     

Realizing whole-body tactile interactions with a self-organizing,multi-modal artificial skin on a humanoid robot

In this paper, we present a new approach to realize whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot. We, therefore, equipped the whole upper body of the humanoid HRP-2 with various patches of CellulARSkin – a modular artificial skin. In order to automatically handle a potentially high number of tactile sensor cells and motors units, the robot uses open-loop exploration motions, and distributed accelerometers in the artificial skin cells, to acquire its self-centered sensory-motor knowledge. This body self-knowledge is then utilized to transfer multi-modal tactile stimulations into reactive body motions. Tactile events provide feedback on changes of contact on the whole-body surface. We demonstrate the feasibility of our approach on a humanoid, here HRP-2, grasping large and unknown objects only via tactile feedback. Kinesthetically taught grasping trajectories, are reactively adapted to the size and stiffness of different test objects. Our paper contributes the first realization of a self-organizing tactile sensor-behavior mapping on a full-sized humanoid robot, enabling a position controlled robot to compliantly handle objects.     

Posture/Walking Control for Humanoid Robot Based on Kinematic Resolution of CoM Jacobian With Embedded Motion

This paper proposes the walking pattern generation method, the kinematic resolution method of center of mass (CoM) Jacobian with embedded motions, and the design method of posture/walking controller for humanoid robots. First, the walking pattern is generated using the simplified model for bipedal robot. Second, the kinematic resolution of CoM Jacobian with embedded motions makes a humanoid robot balanced automatically during movement of all other limbs. Actually, it offers an ability of whole body coordination to humanoid robot. Third, the posture/walking controller is completed by adding the CoM controller minus the zero moment point controller to the suggested kinematic resolution method. We prove that the proposed posture/walking controller brings the disturbance input-to-state stability for the simplified bipedal walking robot model. Finally, the effectiveness of the suggested posture/walking control method is shown through experiments with regard to the arm dancing and walking of humanoid robot.     

仿人机器人跑步稳定性准则

A humanoid robot has high mobility but possibly risks of tipping over. Until now, one main topic on humanoid robots is to study the walking stability; the issue of the running stability has rarely been investigated. The running is different from the walking, and is more difficult to maintain its dynamic stability. The objective of this paper is to study the stability criterion for humanoid running based on the whole dynamics. First, the cycle and the dynamics of running are analyzed. Then, the stability criterion of humanoid running is presented. Finally, the effectiveness of the proposed stability criterion is illustrated by a dynamic simulation example using a dynamic analysis and design system (DADS).     

Optimization-based locomotion planning,estimation, and control design for the atlas humanoid robot

This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.     

Whole-Body Motion Generation Integrating Operator's Intention and Robot's Autonomy in Controlling Humanoid Robots

This paper introduces a framework for whole-body motion generation integrating operator's control and robot's autonomous functions during online control of humanoid robots. Humanoid robots are biped machines that usually possess multiple degrees of freedom (DOF). The complexity of their structure and the difficulty in maintaining postural stability make the whole-body control of humanoid robots fundamentally different from fixed-base manipulators. Getting hints from human conscious and subconscious motion generations, the authors propose a method of generating whole-body motions that integrates the operator's command input and the robot's autonomous functions. Instead of giving commands to all the joints all the time, the operator selects only the necessary points of the humanoid robot's body for manipulation. This paper first explains the concept of the system and the framework for integrating operator's command and autonomous functions in whole-body motion generation. Using the framework, autonomous functions were constructed for maintaining postural stability constraint while satisfying the desired trajectory of operation points, including the feet, while interacting with the environment. Finally, this paper reports on the implementation of the proposed method to teleoperate two 30-DOF humanoid robots, HRP-1S and HRP-2, by using only two 3-DOF joysticks. Experiments teleoperating the two robots are reported to verify the effectiveness of the proposed method.     

Learning to Walk by Imitation in Low-Dimensional Subspaces

In this paper, we provide the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data without any prior information of its dynamics model. Programming a humanoid robot to perform an action (such as walking) that takes into account the robot's complex dynamics is a challenging problem. Traditional approaches typically require highly accurate prior knowledge of the robot's dynamics and environment in order to devise complex (and often brittle) control algorithms for generating a stable dynamic motion. Training using human mocap is an intuitive and flexible approach to programming a robot, but direct usage of mocap data usually results in dynamically unstable motion. Furthermore, optimization using high-dimensional mocap data in the humanoid full-body joint space is typically intractable. We propose a new approach to tractable imitation-based learning in humanoids without a robot's dynamic model. We represent kinematic information from human mocap in a low-dimensional subspace and map motor commands in this low-dimensional space to sensory feedback to learn a predictive dynamic model. This model is used within an optimization framework to estimate optimal motor commands that satisfy the initial kinematic constraints as best as possible while generating dynamically stable motion. We demonstrate the viability of our approach by providing examples of dynamically stable walking learned from mocap data using both a simulator and a real humanoid robot.     

基于全身协调的仿人机器人步行稳定控制

提出利用机器人质心（CoM）雅克比矩阵,实现全身协调补偿的算法。提出机器人的简化模型;分析基于CoM雅克比矩阵的补偿算法;采用CoM/ZMP（零点矩点）、减振和软着陆控制器实时控制双足步行,实现机器人全身协调的稳定控制;通过仿人机器人AFU09的双足步行实验证明该控制方法的有效性。     

Force feedback exploiting tactile and proximal force/torque sensing

The paper addresses the problem of measuring whole-body dynamics for a multiple-branch kinematic chain in presence of unknown external wrenches. The main result of the paper is to give a methodology for computing whole body dynamics by aligning a model of the system dynamics with the measurements coming from the available sensors. Three primary sources of information are exploited: (1) embedded force/torque sensors, (2) embedded inertial sensors, (3) distributed tactile sensors (i.e. artificial skin). In order to cope with external wrenches applied at continuously changing locations, we model the kinematic chain with a graph which dynamically adapts to the contact locations. Classical pre-order and post-order traversals of this dynamically evolving graph allow computing whole-body dynamics and estimate external wrenches. Theoretical results have been implemented in an open-source software library (iDyn) released under the iCub project. Experimental results on the iCub humanoid robot show the effectiveness of the proposed approach.     

Implementation and stability analysis of prioritized whole-body compliant controllers on a wheeled humanoid robot in uneven terrains

In this work, we implement the floating base prioritized whole-body compliant control framework described in Sentis et al. (IEEE Transactions on Robotics 26(3):483–501, 2010) on a wheeled humanoid robot maneuvering in sloped terrains. We then test it for a variety of compliant whole-body behaviors including balance and kinesthetic mobility on irregular terrain, and Cartesian hand position tracking using the co-actuated (i.e. two joints are simultaneously actuated with one motor) robot’s upper body. The implementation serves as a hardware proof for a variety of whole-body control concepts that had previously been developed and tested in simulation. First, behaviors of two and three priority tasks are implemented and successfully executed on the humanoid hardware. In particular, first and second priority tasks are linearized in the task space through model feedback and then controlled through task accelerations. Postures, on the other hand, are shown to be asymptotically stable when using prioritized whole-body control structures and then successfully tested in the real hardware. To cope with irregular terrains, the base is modeled as a six degree of freedom floating system and the wheels are characterized through contact and rolling constraints. Finally, center of mass balance capabilities using whole-body compliant control and kinesthetic mobility are implemented and tested in the humanoid hardware to climb terrains with various slopes.     

人体步行规律与仿人机器人步态规划

从仿生学角度分析了人体的步行运动规律,提出了一种基于人体运动规律的仿人机器人步态参数设定方法.首先对人体步行运动数据进行捕捉并分析,得出人体各步态参数间的函数关系,以人体步行相似性作为评价指标,提出仿人机器人步态参数的设定方法.其次,通过分析人体在步行过程中的补偿支撑脚偏航力矩的基本原理,提出了基于双臂及腰关节协调运动的仿人机器人偏航力矩补偿算法,以提高仿人机器人行走的稳定性.最后通过仿真及实验验证了所提出的步态规划方法的正确性及有效性.     

Development of flexible joints for a humanoid robot that walks on an oscillating plane

In this study, we develop flexible joints for a humanoid robot that walks on an oscillating plane and discuss their effectiveness in compensating disturbances. Conventional robots have a rigid frame and are composed of rigid joints driven by geared motors. Therefore, disturbances, which may be caused by external forces from other robots, obstacles, vibration and oscillation of the surface upon which the robot is walking, and so on, are transmitted directly to the robot body, causing the robot to fall. To address this problem, we focus on a flexible mechanism. We develop flexible joints and incorporate them in the waist of a humanoid robot; the experimental task of the robot is to walk on a horizontally oscillating plane until it reaches the desired position. The robot with the proposed flexible joints, reached the goal position despite the fact that the controller was the same as that used for a conventional robot walking on a static plane. From these results, we conclude that our proposed mechanism is effective for humanoid robots that walk on an oscillating plane.     

基于最优控制的仿人机器人行走振动抑制

针对仿人机器人行走过程中由腿部非刚性特性引起的振动,提出了一种基于最优控制的行走振动抑制方法．首先对振动进行建模,并将这一模型加入到原有机器人动力学模型中去．然后基于拓展的动力学模型,使用预观控制方法求取一条符合质心加速度约束的控制轨迹,作为求解最优控制问题的初始解．进而由此初始解出发,迭代求解此带约束的最优控制问题,利用得到的最优控制轨迹即能以前馈方式抑制行走过程中的振动．最后,使用预观控制方法和本文方法在仿人机器人“空”上进行行走对比实验．实验结果表明提出的方法显著减小了机器人行走过程中零力矩点（ZMP）的振荡和躯干的晃动．该方法对行走振动抑制的有效性得到了验证．     

Autonomous online generation of a motor representation of the workspace for intelligent whole-body reaching

We describe a learning strategy that allows a humanoid robot to autonomously build a representation of its workspace: we call this representation Reachable Space Map. Interestingly, the robot can use this map to: (i) estimate the Reachability of a visually detected object (i.e. judge whether the object can be reached for, and how well, according to some performance metric) and (ii) modify its body posture or its position with respect to the object to achieve better reaching. The robot learns this map incrementally during the execution of goal-directed reaching movements; reaching control employs kinematic models that are updated online as well. Our solution is innovative with respect to previous works in three aspects: the robot workspace is described using a gaze-centered motor representation, the map is built incrementally during the execution of goal-directed actions, learning is autonomous and online. We implement our strategy on the 48-DOFs humanoid robot Kobian and we show how the Reachable Space Map can support intelligent reaching behavior with the whole-body (i.e. head, eyes, arm, waist, legs).     

基于运动相似性的仿人机器人双足步行研究

提出了一种基于人体步行运动相似性的仿人机器人双足步行动作设计方法．改进了人体步行轨迹的参 数获取与相似性匹配系统,扩展了相似性函数的适用范围．根据仿人机器人的机械连杆特点定义了步行运动周期中 的关键姿势与子相变换,建立了运动学约束方程,并对行走中出现的动态稳定性问题进行了约束．仿真和实体机器 人实验验证了该方法的有效性．     

Pivoting based manipulation by a humanoid robot

In this paper we address whole-body manipulation of bulky objects by a humanoid robot. We adopt a “pivoting” manipulation method that allows the humanoid to displace an object without lifting, but by the support of the ground contact. First, the small-time controllability of pivoting is demonstrated. On its basis, an algorithm for collision-free pivoting motion planning is established taking into account the naturalness of motion as nonholonomic constraints. Finally, we present a whole-body motion generation method by a humanoid robot, which is verified by experiments.     

Somatosensory computation for man-machine interface from motion-capture data and musculoskeletal human model

We discuss the computation of somatosensory information from motion-capture data. The efficient computational algorithms previously developed by the authors for multibody systems, such as humanoid robots, are applied to a musculoskeletal model of the human body. The somatosensory information includes tension, length, and velocity of the muscles, tension of the tendons and ligaments, pressure of the cartilages, and stress of the bones. The inverse dynamics of the musculoskeletal human model is formulated as an optimization problem subject to equality and inequality conditions. We analyzed the solutions obtained by linear and quadratic programming methods, and showed that linear programming has better performance. The technological development aims to define a higher dimensional man-machine interface and to open the door to the cognitive-level communication of humans and machines.