Experience-based imitation using RNNPB

Robot imitation is a useful and promising alternative to robot programming. Robot imitation involves two crucial issues. The first is how a robot can imitate a human whose physical structure and properties differ greatly from its own. The second is how the robot can generate various motions from finite programmable patterns (generalization). This paper describes a novel approach to robot imitation based on its own physical experiences. We considered the target task of moving an object on a table. For imitation, we focused on an active sensing process in which the robot acquires the relation between the object's motion and its own arm motion. For generalization, we applied the RNNPB (recurrent neural network with parametric bias) model to enable recognition/generation of imitation motions. The robot associates the arm motion which reproduces the observed object's motion presented by a human operator. Experimental results proved the generalization capability of our method, which enables the robot to imitate not only motion it has experienced, but also unknown motion through nonlinear combination of the experienced motions.     

Unfolding of a rectangular cloth from unarranged starting shapes by a Dual-Armed robot with a mechanism for managing recognition error and uncertainty

We propose a method for unfolding a rectangular cloth placed on a table in an arbitrary unarranged shape, using a dual arm robot. There are many situations where the manipulation of fabric products by dual arm robots is slow due to operation complexity. Also, observation of fabric products in unarranged shapes can be fraught with uncertainty, posing further difficulties for robotic manipulation. In this article, we address these problems for our specific task, implementing a ‘pinch and slide motion’ to address the former issue, and an operation selection mechanism implemented as a partially observable Markov decision process to address the latter. We used this approach to let a robot unfold a rectangular cloth, thereby experimentally verifying the effectiveness of our approach.     

Robot arm-wrist coordination. Part 1. Quantitative rating

The robotic capability for controlled motion depends upon the robot's ability to coordinate its arm and wrist in order to accomplish the desired task. The objective of this article is to define formally the arm-wrist coordination and to introduce a quantitative measure for it. We develop a mathematical framework that provides for the analysis of the impact of both the fixed manipulator geometry and the changing robot configuration upon the efficiency of arm-wrist coordination. In the companion paper (Part 2). manipulator design guidelines are then formulated to guarantee task decomposition for any desired robot task. Numerical simulations demonstrate the efficacy of these design guidelines.     

Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach

We propose an approach to efficiently teach robots how to perform dynamic manipulation tasks in cooperation with a human partner. The approach utilises human sensorimotor learning ability where the human tutor controls the robot through a multi-modal interface to make it perform the desired task. During the tutoring, the robot simultaneously learns the action policy of the tutor and through time gains full autonomy. We demonstrate our approach by an experiment where we taught a robot how to perform a wood sawing task with a human partner using a two-person cross-cut saw. The challenge of this experiment is that it requires precise coordination of the robot’s motion and compliance according to the partner’s actions. To transfer the sawing skill from the tutor to the robot we used Locally Weighted Regression for trajectory generalisation, and adaptive oscillators for adaptation of the robot to the partner’s motion.     

Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

Performing manipulation tasks interactively in real environments requires a high degree of accuracy and stability. At the same time, when one cannot assume a fully deterministic and static environment, one must endow the robot with the ability to react rapidly to sudden changes in the environment. These considerations make the task of reach and grasp difficult to deal with. We follow a Programming by Demonstration (PbD) approach to the problem and take inspiration from the way humans adapt their reach and grasp motions when perturbed. This is in sharp contrast to previous work in PbD that uses unperturbed motions for training the system and then applies perturbation solely during the testing phase. In this work, we record the kinematics of arm and fingers of human subjects during unperturbed and perturbed reach and grasp motions. In the perturbed demonstrations, the target’s location is changed suddenly after the onset of the motion. Data show a strong coupling between the hand transport and finger motions. We hypothesize that this coupling enables the subject to seamlessly and rapidly adapt the finger motion in coordination with the hand posture. To endow our robot with this competence, we develop a coupled dynamical system based controller, whereby two dynamical systems driving the hand and finger motions are coupled. This offers a compact encoding for reach-to-grasp motions that ensures fast adaptation with zero latency for re-planning. We show in simulation and on the real iCub robot that this coupling ensures smooth and “human-like” motions. We demonstrate the performance of our model under spatial, temporal and grasp type perturbations which show that reaching the target with coordinated hand–arm motion is necessary for the success of the task.     

基于DSP的机器人末端力控制系统设计

工业机器人的运动分为自由运动和受力约束运动两种不同的运动类型;受力约束运动不仅要进行精确的位置控制,而且要对接触力进行精确控制;文章对机械臂的末端力控制系统进行了研究;采用了高精度电动滑台作为力控制系统的执行机构,在电动滑台末端增加了柔性单元,从而克服了系统柔性不足的缺点,并且依据Lyapunov稳定性理论确定系统的二阶参数,使用Narendra提出的稳定自适应设计方法构建了电动滑台的数学模型;对机械臂末端柔顺系统的系统设计及算法进行了研究,消除了外界噪声对系统的干扰,解决了力控制的基本控制策略问题,优化了力控制时系统的响应特性。     

Robot arm-wrist coordination. Part 2. Design guidelines

In the companion article (Part 1), we developed a mathematical framework that provides for the analysis and quantitative rating of arm-wrist coordination. The objective of this article is to exploit this framework and formulate manipulator design guidelines for effective arm-wrist coordination and task execution. We explore various arm and wrist designs and quantify their performance in terms of meeting the two design objectives of efficient arm-wrist coordination and robot flexibility. Numerical simulation experiments highlight the measurable effects of both the fixed manipulator geometry and changing robot configuration on arm-wrist coordination and demonstrate the impact of the design guidelines on robot performance.     

Bilateral remote teaching and autonomous task execution with task progress feedback

This paper describes an approach to estimating the progress in a task executed by a humanoid robot and to synthesizing motion based on the current progress so that the robot can achieve the task. The robot observes a human performing whole body motion for a specific task, and encodes these motions into a hidden Markov model (HMM). The current observation is compared with the motion generated by the HMM, and the task progress can be estimated during the robot performing the motion. The robot subsequently uses the estimate of the task progress to generate a motion appropriate to the current situation with the feedback rule. We constructed a bilateral remote control system with humanoid robot HRP-4 and haptic device Novint Falcon, and we made the humanoid robot push a button. Ten trial motions of pushing a button were recorded for the training data. We tested our proposed approach on the autonomous execution of the pushing motion by the humanoid robot, and confirmed the effectiveness of our task progress feedback method.     

Motion planning and contact control for a tele-assisted hydraulic underwater robot

Implementing tele-assistance or supervisory control for autonomous subsea robots requires atomic actions that can be called from high level task planners or mission managers. This paper reports on the design and implementation of a particular atomic action for the case of a subsea robot carrying out tasks in contact with the surrounding environment.Subsea vehicles equipped with manipulators can have upward of 11 degrees of freedom (DOF), with degenerate and redundant inverse kinematics. Distributed local motion planning is presented as a means to specify the motion of each robot DOF given a goal point or trajectory. Results are presented to show the effectiveness of the distributed versus non-distributed approach, a means to deal with local minima difficulties, and the performance for trajectory following with and without saturated joint angles on a robot arm.Consideration is also given to the modelling of hydraulic underwater robots and to the resulting design of hybrid position/force control strategies. A model for a hydraulically actuated robot is developed, taking into account the electrohydraulic servovalve, the bulk modulus of oil, piston area, friction, hose compliance and other arm parameters. Open and closed-loop control results are reported for simulated and real systems.Finally, the use of distributed motion planning and sequential position/force control of a Slingsby TA-9 hydraulic underwater manipulator is described, to implement an atomic action for tele-assistance. The specific task of automatically positioning and inserting a Tronic subsea mateable connector is illustrated, with results showing the contact conditions during insertion.     

Learning to Play Minigolf: A Dynamical System-Based Approach

A current trend in robotics is to define robot motions so that they can be easily adopted to situations beyond those for which the motion was originally designed. In this work, we show how the challenging task of playing minigolf can be efficiently tackled by first learning a basic hitting motion model, and then learning to adapt it to different situations. We model the basic hitting motion with an autonomous dynamical systems, and solve the problem of learning the parameters of the model from a set of demonstrations through a constrained optimization. To hit the ball with the appropriate hitting angle and speed, a nonlinear model of the hitting parameters is estimated based on a set of examples of good hitting parameters. We compare two statistical methods, Gaussian Process Regression and Gaussian Mixture Regression in the context of inferring the hitting parameters for the minigolf task. We demonstrate the generalization ability of the model in various situations. We validate our approach on the 7 Degrees of Freedom (DoF) Barrett WAM arm and 6-DoF Katana arm in both simulated and real environments.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.     

Motion coordination and reactive control of autonomous multi-manipulator systems

The emerging field of service robots demands new systems with increased flexibility. The flexibility of a robot system can be increased in many different ways. Mobile manipulation—the coordinated use of manipulation capabilities and mobility—is an approach to increase robots flexibility with regard to their motion capabilities. Most mobile manipulators that are currently under development use a single arm on a mobile platform. The use of a two-arm manipulator system allows increased manipulation capabilities, especially when large, heavy, or non-rigid objects must be manipulated. This article is concerned with motion control for mobile two-arm systems. These systems require new schemes for motion coordination and control. A coordination scheme called transparent coordination is presented that allows for an arbitrary number of manipulators on a mobile platform. Furthermore, a reactive control scheme is proposed to enable the platform to support sensor-guided manipulator motion. Finally, this article introduces a collision avoidance scheme for mobile two-arm robots. This scheme surveys the vehicle motion to avoid platform collisions and arm collisions caused by self-motion of the robot. © 1996 John Wiley & Sons, Inc.     

Reusing Primitive and Acquired Motion Knowledge for Gait Generation of a Six-legged Robot Using Genetic Programming

There has been growing interest in motion planning problems for mobile robots. In this field, the main research is to generate a motion for a specific robot and task without previously acquired motions. However it is too wasteful not to use hard-earned acquired motions for other tasks. Here, we focus on a mechanism of reusing acquired motion knowledge and study a motion planning system able to generate and reuse motion knowledge. In this paper, we adopt a tree-based representation for expressing knowledge of motion, and propose a hierarchical knowledge for realizing a reuse mechanism. We construct a motion planning system using hierarchical knowledge as motion knowledge and using genetic programming as a learning method. We apply a proposed method for the gait generation task of a six-legged locomotion robot and show its availability with computer simulation.     

Dynamic path planning for a planar articulated robot arm moving amidst unknown obstacles

This work is concerned with planning collision-free paths for a robot arm moving in an environment filled with unknown obstacles, where any point of the robot body is subject to collision. To compensate for the uncertainty, the system is provided with sensory feedback information about its immediate surroundings. In such a setting, which presents significant practical and theoretical interest, human intuition is of little help, and designing algorithms with proven convergence thus becomes an important task. We show that, given the target position, local feedback information is sufficient to guarantee reaching a global objective (the target position) and present a nonheuristic algorithm which generates reasonable—if, in general, not optimal—collision-free paths. In this approach, the path is being planned continuously (dynamically), based on the arm's current position and on the sensory feedback. Here, a case of a planar arm with two revolute joints is studied. No constraints on the shape of the robot links or the obstacles are imposed. The general idea is to reduce the problem of motion planning to an analysis of simple closed curves on the surface of an appropriate two-dimensional manifold.     

Measurement and storage of a network of jacobians as a method for the visual positioning of a robot arm

The goal of this paper is to describe a method to position a robot arm at any visible point of a given workspace without an explicit on line use of the analytical form of the transformations between real space and camera coordinates (camera calibration) or between cartesian and joint coordinates (direct or inverse kinematics of the robot arm). The formulation uses a discrete network of points distributed all over the workspace in which a procedure is given to measure certain Jacobian matrices which represent a good local linear approximation to the unknown compound transformation between camera and joint coordinates. This approach is inspired by the biological observation of the vestibulo-ocular reflex in humans (VOR). We show that little space is needed to store the transformation at a given scale, as feedback on the visual coordinates is used to improve precision up to the limit of the visual system. This characteristic also allows the plant to cope with disturbances in camera positioning or robot parameters. Furthermore, if the dimension of the visual space is equal or bigger than the motor space dimension, the transformation can be inverted, resulting in a realistic model of the plant able to be used to train other methods for the determination of visuo-motor mapping. As a test of the method an experiment to position a real robot arm is presented, together with another experiment showing the robot executing a simple task (building a tower of blocks).     

双臂搬运机器人反应式导航控制系统设计

为增强双臂搬运机器人在作业任务过程中的行进避障能力,使其运动行为得到连续有效控制,设计双臂搬运机器人的反应式导航控制系统。根据单片机与电机电路的连接形式,选择合适的ARM微处理器元件与PIC单片机结构,再联合HN-9移动平台、智能导航平台、ROS操作平台,完善反应式导航子模块的运行能力,实现控制系统的硬件单元设计。求取绝对位姿向量、相对位姿向量的计算结果,以此作为自变量系数,确定速度雅可比指标,并推断得出动力学递推表达式,完成对双臂搬运机器人的协调控制,联合相关硬件应用结构,实现双臂搬运机器人反应式导航控制系统的设计。对比实验结果：反应式导航控制系统可使机器人准确躲避行进障碍物,且躲避过程中机器人完成作业任务的能力不会受到影响,符合连续有效控制机器人搬运行为的实际应用需求。     

基于STFT的微创手术机器人运动控制系统设计

针对微创手术机器人运动过程受到信号干扰而导致机器入主从臂控制效果差、控制精度较低的问题,提出了基于STFT的微创手术机器人运动控制系统设计;主控制器通过蓝牙的无线传输方式与主机连接,采用I/O口模拟SCI硬件流控方式,实现MCU与蓝牙通信,为系统提供基础数据;设计时钟基准电路的并联振荡模式,方便失控程序重启;根据运动控制器结构,设计预留模数转换接口,并对机器人主从操作臂展开详细分析;使用基于STFT短时傅里叶变换方法抑制外界干扰,在LM629运动控制专用集成芯片支持下设计控制流程,完成基于STFT的微创手术机器人运动控制系统设计;设计对比实验,结果表明该系统与期望主臂运动轨迹的终点坐标(10 mm,-35 mm,45 mm)和从臂运动轨迹的终点坐标(18 mm,-45 mm,25 mm)一致,能够精准控制微创手术机器人运动精度,为医学手术智能化开展提供设备支持.     

A singularity-free motion control algorithm for robot manipulators—a hybrid system approach

This paper presents the design and implementation of a singularity-free tracking algorithm for robot manipulators using a hybrid system approach. A hybrid robot motion controller is designed to ensure feasible robot motion in the neighborhood of kinematic singularities. The hybrid control system has a two-layered hierarchical structure, a discrete layer and a continuous layer. The robot workspace is partitioned into subspaces based on the singular configurations of the robot. Switching between continuous controllers is involved when the robot travels across the subspaces. With the hybrid controller, the robot can work at the vicinity of singular configurations, but also can go through and stay at the singular configurations. The stability of the hybrid system is investigated using multiple Lyapunov function theory. Experimental results have demonstrated the advantages of the hybrid robot motion control method.     

Executing optimized throwing motion on robot arm with free joint

We address the throwing motion optimization for robot. In order to pursue the best throwing motion, we may need heuristics/intuition free methods. We propose a throwing method that is composed of rapid semi-optimal motion-planning and output zeroing method. So as to execute the optimized trajectories in real rigid body systems, we need some compensations for the noises around the optimized trajectories. We introduce a compensation method for the optimized throwing motions of a robot arm with a free joint. To validate the effectiveness of the proposed method, we conducted a throwing experiment using a two-link arm. As a result of the experiment, the robot arm threw a ball with 63.7 km/h, which was the best record through the past experiments of this arm.     

Markerless human–robot interface for dual robot manipulators using Kinect sensor

Remote teleoperation of robot manipulators is often necessary in unstructured, dynamic, and dangerous environments. However, the existing mechanical and other contacting interfaces require unnatural, or hinder natural, human motions. At present, the contacting interfaces used in teleoperation for multiple robot manipulators often require multiple operators. Previous vision-based approaches have only been used in the remote teleoperation for one robot manipulator as well as require the special quantity of illumination and visual angle that limit the field of application. This paper presents a noncontacting Kinect-based method that allows a human operator to communicate his motions to the dual robot manipulators by performing double hand–arm movements that would naturally carry out an object manipulation task. This paper also proposes an innovative algorithm of over damping to solve the problem of error extracting and dithering due to the noncontact measure. By making full use of the human hand–arm motion, the operator would feel immersive. This human–robot interface allows the flexible implementation of the object manipulation task done in collaboration by dual robots through the double hand–arm motion by one operator.