Dynamic model for using casterboard by a humanoid robot

ABSTRACT

The efficacy of traveling and movement is substantially enhanced by the use of wheel mechanisms, particularly when compared to bipedal locomotion. Humanoid locomotion can also be extended using appropriate tools. In this study, we focus on the casterboard as a tool that facilitates traveling, because it has comparatively high portability, and the rider can move forward without pushing off the ground. We present a dynamic model for the use of the casterboard in a humanoid robot. Given that the ankles and waist of the rider play a key role in traveling using the casterboard, the angles of those joints are utilized as the input of the proposed model. A simplified small-scale robot is used to determine essential elements, and a modified casterboard is employed to eliminate the risk of falling without loss of its unique properties, for verification of the dynamic model. Using simulations and experiments, it is demonstrated that the dynamic model effectively emulates the behavior of the actual setup with respect to forward movement and turning. The model serves as the basis for the planning of a reference trajectory using the casterboard.     

Policy gradient learning for a humanoid soccer robot

In humanoid robotic soccer, many factors, both at low-level (e.g., vision and motion control) and at high-level (e.g., behaviors and game strategies), determine the quality of the robot performance. In particular, the speed of individual robots, the precision of the trajectory, and the stability of the walking gaits, have a high impact on the success of a team. Consequently, humanoid soccer robots require fine tuning, especially for the basic behaviors. In recent years, machine learning techniques have been used to find optimal parameter sets for various humanoid robot behaviors. However, a drawback of learning techniques is time consumption: a practical learning method for robotic applications must be effective with a small amount of data. In this article, we compare two learning methods for humanoid walking gaits based on the Policy Gradient algorithm. We demonstrate that an extension of the classic Policy Gradient algorithm that takes into account parameter relevance allows for better solutions when only a few experiments are available. The results of our experimental work show the effectiveness of the policy gradient learning method, as well as its higher convergence rate, when the relevance of parameters is taken into account during learning.     

On learning, representing, and generalizing a task in a humanoid robot.

We present a programming-by-demonstration framework for generically extracting the relevant features of a given task and for addressing the problem of generalizing the acquired knowledge to different contexts. We validate the architecture through a series of experiments, in which a human demonstrator teaches a humanoid robot simple manipulatory tasks. A probability-based estimation of the relevance is suggested by first projecting the motion data onto a generic latent space using principal component analysis. The resulting signals are encoded using a mixture of Gaussian/Bernoulli distributions (Gaussian mixture model/Bernoulli mixture model). This provides a measure of the spatio-temporal correlations across the different modalities collected from the robot, which can be used to determine a metric of the imitation performance. The trajectories are then generalized using Gaussian mixture regression. Finally, we analytically compute the trajectory which optimizes the imitation metric and use this to generalize the skill to different contexts.     

Real-world reinforcement learning for autonomous humanoid robot docking

Reinforcement learning (RL) is a biologically supported learning paradigm, which allows an agent to learn through experience acquired by interaction with its environment. Its potential to learn complex action sequences has been proven for a variety of problems, such as navigation tasks. However, the interactive randomized exploration of the state space, common in reinforcement learning, makes it difficult to be used in real-world scenarios. In this work we describe a novel real-world reinforcement learning method. It uses a supervised reinforcement learning approach combined with Gaussian distributed state activation. We successfully tested this method in two real scenarios of humanoid robot navigation: first, backward movements for docking at a charging station and second, forward movements to prepare grasping. Our approach reduces the required learning steps by more than an order of magnitude, and it is robust and easy to be integrated into conventional RL techniques.     

仿人机器人动态步行控制综述

综述了仿人机器人动态步行的研究历史和研究现状。归纳了动态步行的特点,分析了动态步行稳定性判定方法,介绍了基于ZMP的姿态稳定判据和基于庞加莱映射（Poincaré Map）的步态稳定判据。总结了仿人机器人学习适应复杂地面环境步行的方法,概述了动态步行控制实现的典型解决方案,指出了动态步行控制中待解决的问题,并探讨了未来的发展方向。     

基于体感的仿人机器人步态学习与控制

针对现有理想化步态动力学模型规划方法复杂、人为指定参数过多、计算量大的问题,提出一种基于体感数据学习人体步态的仿人机器人步态生成方法。首先,用体感设备收集人体骨骼信息,基于最小二乘拟合方法建立人体关节局部坐标系;其次,搭建人体与机器人映射的运动学模型,根据两者间主要关节映射关系,生成机器人关节转角轨迹,实现机器人对人类行走姿态的学习;然后,基于零力矩点(ZMP)稳定性原则,对机器人脚踝关节转角采用梯度下降算法进行优化控制;最后,在步态稳定性分析上,提出使用安全系数来评价机器人行走稳定程度的方法。实验结果表明,步行过程中安全系数保持在0~0.85,期望为0.4825,ZMP接近于稳定区域中心,机器人实现了仿人姿态的稳定行走,证明了该方法的有效性。     

Online tracking and mimicking of human movements by a humanoid robot

This paper describes a humanoid robot system that can capture and mimic the motion of human body parts in real-time. The underlying vision system is able to automatically detect and track human body parts such as hands and faces in images captured by the robot's eyes. It is based on a probabilistic approach that can detect and track multiple blobs in a 60-Hz stereo image stream on a standard dual processor PC. A random jerk model is employed to approximate the observed human motion and a Kalman filter is used to estimate its parameters (three-dimensional positions, velocities and accelerations). This enables the system to realistically mimic the perceived motion in real-time.     

基于HMCD的仿人机器人单杠运动控制策略

为完成仿人机器人单杠运动,分析了欠驱动单杠机器人Acrobot模型,并根据IHOG技术要求、实物机器人本体结构和自由度配置,提出了基于HMCD的控制策略.通过单杠视频捕捉获取人体运动数据,根据仿人机器人模型分析关键特征点、基本动作的运动数据得到的关键帧的关节角数据,经过运动学约束调整,采用插值方法生成能够应用于仿人机器人的运动轨迹.在MF-1型仿人机器人单杠实物平台上进行控制实验的成功,验证了该方法的有效性.     

Evaluation of active wearable assistive devices with human posture reproduction using a humanoid robot*

This study proposes a quantitative evaluation method for assessing active wearable assistive devices that can efficiently support the human body. We utilize a humanoid robot to simulate human users wearing assistive devices owing to various advantages offered by the robot such as quantitative torque measurement from sensors and highly repeatable motion. In this study, we propose a scheme for estimating the supportive torques supplied by a device called stationary torque replacement. To validate the reliability of this evaluation method by using a humanoid robot, we conducted measurements of human muscular activity during assisted motion. Analysis of the measured muscle activity revealed that a humanoid robot closely simulates the actual usage of assistive devices. Finally, we showed the feasibility of the proposed evaluation method through an experiment with the humanoid robot platform HRP-4 and the Muscle Suit active assistive device. With the proposed method, the supportive effects of the assistive device could be measured quantitatively in terms of the static supportive torque acting directly on the body of a simulated human user.     

Centroidal dynamics of a humanoid robot

The center of mass (CoM) of a humanoid robot occupies a special place in its dynamics. As the location of its effective total mass, and consequently, the point of resultant action of gravity, the CoM is also the point where the robot’s aggregate linear momentum and angular momentum are naturally defined. The overarching purpose of this paper is to refocus our attention to centroidal dynamics: the dynamics of a humanoid robot projected at its CoM. In this paper we specifically study the properties, structure and computation schemes for the centroidal momentum matrix (CMM), which projects the generalized velocities of a humanoid robot to its spatial centroidal momentum. Through a transformation diagram we graphically show the relationship between this matrix and the well-known joint-space inertia matrix. We also introduce the new concept of “average spatial velocity” of the humanoid that encompasses both linear and angular components and results in a novel decomposition of the kinetic energy. Further, we develop a very efficient $O(N)$ O ( N ) algorithm, expressed in a compact form using spatial notation, for computing the CMM, centroidal momentum, centroidal inertia, and average spatial velocity. Finally, as a practical use of centroidal dynamics we show that a momentum-based balance controller that directly employs the CMM can significantly reduce unnecessary trunk bending during balance maintenance against external disturbance.     

基于三质心模型的类人机器人射门算法

为了实现快速稳定的射门动作,提出了一种基于三质心模型的类人机器人射门轨迹规划方法。首先,根据三质心模型,得到包含游动腿轨迹和躯干轨迹的零力矩点(ZMP)方程, 采用三次贝塞尔曲线规划游动腿轨迹和ZMP轨迹,根据ZMP方程求解出类人机器人的躯干轨迹;其次,在双腿支撑相根据线性摆模型计算类人机器人的质心轨迹,实现射门姿态的快速调整;最后,在RoboCup 3D仿真平台中应用此算法实现了类人机器人的快速射门动作,并与其他球队的射门动作进行了对比。实验结果表明：应用该算法仅需手工调试即可快速实现稳定的射门动作,射门动作时间有很大减少,可增强机器人足球队的竞争力。     

Autonomous car driving by a humanoid robot

Enabling a humanoid robot to drive a car requires the development of a set of basic primitive actions. These include walking to the vehicle, manually controlling its commands (e.g., ignition, gas pedal, and steering) and moving with the whole body to ingress/egress the car. We present a sensor‐based reactive framework for realizing the central part of the complete task, consisting of driving the car along unknown roads. The proposed framework provides three driving strategies by which a human supervisor can teleoperate the car or give the robot full or partial control of the car. A visual servoing scheme uses features of the road image to provide the reference angle for the steering wheel to drive the car at the center of the road. Simultaneously, a Kalman filter merges optical flow and accelerometer measurements to estimate the car linear velocity and correspondingly compute the gas pedal command for driving at a desired speed. The steering wheel and gas pedal reference are sent to the robot control to achieve the driving task with the humanoid. We present results from a driving experience with a real car and the humanoid robot HRP‐2Kai. Part of the framework has been used to perform the driving task at the DARPA Robotics Challenge.     

Multi-physics modelling of a compliant humanoid robot

We present a multibody simulator being used for compliant humanoid robot modelling and report our reasoning for choosing the settings of the simulator’s key features. First, we provide a study on how the numerical integration speed and accuracy depend on the coordinate representation of the multibody system. This choice is particularly critical for mechanisms with long serial chains (e.g. legs and arms). Our second contribution is a full electromechanical model of the inner dynamics of the compliant actuators embedded in the COMAN robot, since joints’ compliance is needed for the robot safety and energy efficiency. Third, we discuss the different approaches for modelling contacts and selecting an appropriate contact library. The recommended solution is to couple our simulator with an open-source contact library offering both accurate and fast contact modelling. The simulator performances are assessed by two different tasks involving contacts: a bimanual manipulation task and a squatting tasks. The former shows reliability of the simulator. For the latter, we report a comparison between the robot behaviour as predicted by our simulation environment, and the real one.     

Service-provider intelligent humanoid robot using TCP/IP and CORBA

International Journal of Control, Automation and Systems - Ubiquitous and traditional service-provider platforms and frameworks are non-flexible and standardized. But ubiquitous environments are...     

Adaptive dynamic walking of a quadruped robot using a neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In this paper, we integrate several reflexes, such as a stretch reflex, a vestibulospinal reflex and extensor/flexor reflexes, into a central pattern generator (CPG). We try to realize adaptive walking up and down a slope of 12°, walking over an obstacle 3 cm in height, and walking on terrain undulation consisting of bumps 3 cm in height with fixed parameters of CPGs and reflexes. The success in walking on such irregular terrain in spite of stumbling and landing on obstacles shows that the control method using a neural system model proposed in this study has the ability for autonomous adaptation to unknown irregular terrain. In order to clarify the role of a CPG, we investigate the relation between parameters of a CPG and the mechanical system by simulations and experiments. CPGs can generate stable walking suitable for the mechanical system by receiving inhibitory input as sensory feedback and generate adaptive walking on irregular terrain by receiving excitatory input as sensory feedback. MPEG footage of these experiments can be seen at: http://www.kimura.is.uec.ac.jp.     

Sensor information analysis for a humanoid robot

For a humanoid robot to safely walk in unknown environments, various sensors are used to identify the surface condition and recognize any obstacles. The humanoid robot is not fixed on the surface and the base/orientation of the kinematics change while it is walking. Therefore, if the foot contact changes from the estimated due to the unknown surface condition, the kinematics results are not correct. The robot may not be able to perform the motion commands based on the incorrect surface condition. Some robots have built-in range sensors but it’s difficult to accurately model the surface from the sensor readings because the movement of the robot should be considered and the robot localization should have zero error for correct interpretation of the sensor readings. In this paper, three infrared range sensors are used in order to perceive the floor state. Covariance analysis is incorporated to consider the uncertainties. The accelerometer and gyro sensor are also used in order to detect the moment a foot hits the surface. This information provides correction to the motion planner and robot kinematics when the environment is not modeled correctly.     

Realization of sign language motion using a dual-arm/hand humanoid robot

The recent increase in technological maturity has empowered robots to assist humans and provide daily services. Voice command usually appears as a popular human–machine interface for communication. Unfortunately, deaf people cannot exchange information from robots through vocal modalities. To interact with deaf people effectively and intuitively, it is desired that robots, especially humanoids, have manual communication skills, such as performing sign languages. Without ad hoc programming to generate a particular sign language motion, we present an imitation system to teach the humanoid robot performing sign languages by directly replicating observed demonstration. The system symbolically encodes the information of human hand–arm motion from low-cost depth sensors as a skeleton motion time-series that serves to generate initial robot movement by means of perception-to-action mapping. To tackle the body correspondence problem, the virtual impedance control approach is adopted to smoothly follow the initial movement, while preventing potential risks due to the difference in the physical properties between the human and the robot, such as joint limit and self-collision. In addition, the integration of the leg-joints stabilizer provides better balance of the whole robot. Finally, our developed humanoid robot, NINO, successfully learned by imitation from human demonstration to introduce itself using Taiwanese Sign Language.