Predicting Object Dynamics From Visual Images Through Active Sensing Experiences

Prediction of dynamic features is an important task for determining the manipulation strategies of an object. This paper presents a technique for predicting dynamics of objects relative to the robot's motion from visual images. During the training phase, the authors use the recurrent neural network with parametric bias (RNNPB) to self-organize the dynamics of objects manipulated by the robot into the PB space. The acquired PB values, static images of objects and robot motor values are input into a hierarchical neural network to link the images to dynamic features (PB values). The neural network extracts prominent features that each induce object dynamics. For prediction of the motion sequence of an unknown object, the static image of the object and robot motor value are input into the neural network to calculate the PB values. By inputting the PB values into the closed loop RNNPB, the predicted movements of the object relative to the robot motion are calculated recursively. Experiments were conducted with the humanoid robot Robovie-IIs pushing objects at different heights. The results of the experiment predicting the dynamics of target objects proved that the technique is efficient for predicting the dynamics of the objects.     

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.     

Object Category Acquisition by Dynamic Touch

The acquisition of object categories which underlie the human lexicon is a prerequisite for domestic robots to communicate with users in a human-like manner. The theory of J. J. Gibson inspires the approach to obtain shared categories through interaction with the shared environment, where explorative behaviors of infants play the role of obtaining distinctive features of objects to shape their categories. Although several existing studies have reproduced the exploratory behaviors of infants by robots to investigate their roles in acquiring such categories, those active categorization methods utilized static touches and the recognition tended to fail by changes of contact conditions. This paper introduces another possible approach to object categorization — object category acquisition by dynamic touch. Dynamic touch (e.g., shaking) provides the agent with the information of the whole object to enable quick and robust recognition. The amplitude spectrum of auditory data which humans obtain during shaking is found to be an effective feature for identifying the object categories of differing dynamics, e.g., rigid objects, paper materials and bottles of water, even though the objects within each category vary in size, shape, amount and contact conditions. Experimental results are given to show the validity of the proposed method and future issues are discussed.     

Lexicon acquisition based on object-oriented behavior learning

Studies on lexicon acquisition systems are gaining attention in the search for a natural human–robot interface and a test environment to model the infant lexicon acquisition process. Although various lexicon acquisition systems that ground words to sensory experience have been developed, existing systems have clear limitations on the ability to autonomously associate words to objects. This limitation is due to the fact that categories for words are formed in a passive manner, either by teaching of caregivers or finding similarities in visual features. This paper presents a system for lexicon acquisition through behavior learning. Based on a modified multi-module reinforcement learning system, the robot is able to automatically associate words to objects with various visual features based on similarities in affordances or in functions. The system was implemented on a mobile robot acquiring a lexicon related to different rolling preferences. The experimental results are given and future issues are discussed.     

A tree architecture with hierarchical data processing on a sensor-rich hexapod robot

This paper presents the hardware and software architecture of Golem, a hexapod robot designed as a flexible, scalable, general purpose development and experimenting tool targeted to academia, industry, and defense environments. The system is technologically innovative in its architecture, performance, size and integration, and is industrially promising in its filling the gap between low-performance commercial solutions and costly application-specific proprietary solutions.     

Modeling and Verification of a Bimanual-Coordinated Training System

Recently, much attention has been paid to the development of robots that support bilateral arm training in various patterns. However, traditional bimanual rehabilitation robots usually realized different training modes with the robot providing a corresponding force for the impaired limb or else achieved an active-assisted mode with the healthy limb providing an assisted force for the impaired one. This paper proposes a robot to support bimanual-coordinated training. Different training modes are realized with one limb providing a corresponding force for the other limb. Two upper limbs accomplish symmetric movements in each training mode. Motion tracking training in active-resisted and active-assisted modes was performed on 11 healthy subjects. After bimanual-coordinated training, position tracking precision was significantly improved. The preliminarily results confirmed the feasibility of the system for supporting healthy subjects in performing bimanual-coordinated training tasks and demonstrated the effectiveness of the system in improving bimanual-coordinated performance of healthy subjects. Such a system could be potentially useful for patients who are in need of motor function rehabilitation after incidents such as stroke.     

Application of Genetic Algorithms for biped robot gait synthesis optimization during walking and going up-stairs

Selecting an appropriate gait can reduce consumed energy by a biped robot. In this paper, a Genetic Algorithm gait synthesis method is proposed, which generates the angle trajectories based on the minimum consumed energy and minimum torque change. The gait synthesis is considered for two cases: walking and going up-stairs. The proposed method can be applied for a wide range of step lengths and step times during walking; or step lengths, stair heights and step times for going up-stairs. The angle trajectories are generated without neglecting the stability of the biped robot. The angle trajectories can be generated for other tasks to be performed by the biped robot, like going down-stairs, overcoming obstacles, etc. In order to verify the effectiveness of the proposed method, the results for minimum consumed energy and minimum torque change are compared. A Radial Basis Function Neural Network is considered for the real-time application. Simulations are realized based upon the parameters of the 'Bonten-Maru I'humanoid robot, which is under development in our laboratory. The evaluation by simulations shows that the proposed method has a good performance.     

Towards Written Text Recognition Based on Handwriting Experiences Using a Recurrent Neural Network

In this paper, we propose a model for recognizing written text through prediction of a handwriting sequence. The approach is based on findings in the brain sciences field. When recognizing written text, humans are said to unintentionally trace its handwriting sequence in their brains. Likewise, we aim to create a model that predicts a handwriting sequence from a static image of written text. The predicted handwriting sequence would be used to recognize the text. As the first step towards the goal, we created a model using neural networks, and evaluated the learning and recognition capability of the model using single Japanese characters. First, the handwriting image sequences for training are self-organized into image features using a self-organizing map. The self-organized image features are used to train the neuro-dynamics learning model. For recognition, we used both trained and untrained image sequences to evaluate the capability of the model to adapt to unknown data. The results of two experiments using 10 Japanese characters show the effectivity of the model.     

Redundancy Control of a Humanoid Head for Foveation and Three-Dimensional Object Tracking: A Virtual Mechanism Approach

This paper presents a novel approach for object tracking with a humanoid robot head. The proposed approach is based on the concept of a virtual mechanism, where the real head is enhanced with a virtual link that connects the eye with a point in three-dimensional space. We tested our implementation on a humanoid head with 7 d.o.f. and two rigidly connected cameras in each eye (wide-angle and telescopic). The experimental results show that the proposed control algorithm can be used to maintain the view of an observed object in the foveal (telescopic) image using information from the peripheral view. Unlike other methods proposed in the literature, our approach indicates how to exploit the redundancy of the robot head. The proposed technique is systematic and can be easily implemented on different types of active humanoid heads. The results show good tracking performance regardless of the distance between the object and the head. Moreover, the uncertainties in the kinematic model of the head do not affect the performance of the system.     

The influence of the radial basis function training strategy on the performance of a neural gripper

In this article, a neural network-based grasping system that is able to collect objects of arbitrary shape is introduced. The grasping process is split into three functional blocks: image acquisition and processing, contact point estimation, and contact force determination. The paper focuses on the second block, which contains two neural networks. A competitive Hopfield neural network first determines an approximate polygon for an object outline. These polygon edges are the input for a supervised neural network model [radial basis function (RBF) or multilayer perceptions], which then defines the contact points. Tests were conducted with objects of different shapes, and experimental results suggest that the performance of the neural gripper and its learning rate are significantly influenced by the choice of supervised training model and RBF learning algorithm.     

基于局部特征的多摄像机间目标识别方法

针对多摄像机监控系统中的人体目标识别问题,提出一种利用局部颜色特征和形状特征的目标识别方法.首先基于彩色图像使用Harris角点检测算子检测出目标图像的特征点,之后以每个特征点为中心分别提取其邻域的对光照、视角和姿态变化具有鲁棒性的局部颜色和形状特征,并建立目标外观模型,最后,采用改进的EMD(Earth Mover's Distance)进行目标间相似度的计算,实现多摄像机间的人体目标识别.实验结果表明,采用的改进方法充分优化了目标的颜色和形状特征,使摄像能够达到较高的识别率.     

基于SIFT特征一致性的目标跟踪算法

针对光照条件突然变化情况下混合目标模型Mean Shift算法无法准确跟踪目标的缺点,提出了一种基于SIFT特征一致性的目标跟踪算法.算法用SIFT特征来匹配帧间的感兴趣区域,同时使用包含初始帧信息和前一帧信息的混合目标模型Mean Shift算法计算帧间感兴趣区域的直方图,以直方图分布距离最小为原则计算Mean Sh...     

基于几何形状特征的运动目标判决方法

为了室内安全监控异常报警任务的需要,文章在对运动检测后的二值图像进行形态学后处理,消除细小噪声、平滑物体边界,对处理后仍保留的大面积伪运动目标区域,通过提取和分析面积、纵横比等几何形状特征参数,提出了一种运动目标判决方法.该方法有利于消除虚假报警,对基于安全报警系统的异常检测具有一定的意义.     

基于VB—PRINTER对象的动态报表打印

介绍在VB中引用MSHFlexGrid控件、Printer对象．将显示在MSHFlexGrid控件中的动态报表打印辅出的设计思想与程序源码。     

一种改进的基于ICA特征子空间的目标识别方法

介绍了独立分量分析（ICA）的基本原理和算法,并提出了基于独立分量分析的特征子空间的目标识别方法。该方法首先利用快速独立分量分析（FastICA）算法对训练集目标图像进行ICA分解,据此建立特征子空间,然后根据待识别图像在特征子空间的投影系数进行判别。本文的改进在于根据类内类间距离比值最小化准则进行最有利于分类的特征的优化选择。实验结果显示,和传统方法相比,改进的方法能有效提高识别的准确率和效率。     

基于火焰图像动态特征的火灾识别算法

提出了一种基于火焰图像动态特征的火灾识别算法.首先在HSI空间对捕捉的图像进行彩色分割,通过相邻两帧图像判断视频中是否存在可疑火焰.当有可疑火焰存在时,再连续捕捉多帧图像,进行火焰中心定位并提取火焰动态特征,最后判断是否发生火灾.实验结果表明,该算法具有比较好的鲁棒性,能够有效地提高火灾识别的准确率、降低误报漏报率.     

基于排序支持向量机的多特征融合目标跟踪算法

针对计算机视觉领域的目标跟踪问题,提出一种基于排序支持向量机的多特征融合目标跟踪算法。利用排序支持向量机学习得到排序函数,提取2种不同的图像特征分别构造分类器,使2个排序支持向量机并行预测,分别计算2个分类器的错误率,从而得到分类器权重完成融合。实验结果表明,与目前主流的跟踪算法相比,该算法的跟踪结果更准确,在复杂视频环境下也能对目标进行稳定跟踪,具有较强的鲁棒性。     

基于目标区域的敏感图像特征提取

针对互联网中色情图像传播愈来愈严重的现象,在充分分析色情图像的特征和图像分块处理的基础上,提出了目标区域分割算法。该方法能够有效地提取网络色情图像的特征,具有较高的实用性和研究价值。     

Classification of Known and Unknown Environmental Sounds Based on Self-Organized Space Using a Recurrent Neural Network

Our goal is to develop a system to learn and classify environmental sounds for robots working in the real world. In the real world, two main restrictions pertain in learning. (i) Robots have to learn using only a small amount of data in a limited time because of hardware restrictions. (ii) The system has to adapt to unknown data since it is virtually impossible to collect samples of all environmental sounds. We used a neuro-dynamical model to build a prediction and classification system. This neuro-dynamical model can self-organize sound classes into parameters by learning samples. The sound classification space, constructed by these parameters, is structured for the sound generation dynamics and obtains clusters not only for known classes, but also unknown classes. The proposed system searches on the basis of the sound classification space for classifying. In the experiment, we evaluated the accuracy of classification for both known and unknown sound classes.     

一种基于区域特征动态加权的自适应图像融合方法

提出了一种基于区域特征动态加权的自适应遥感图像融合方法。该方法首先由输入图像得到其高频、低频图像；进而分别选取一组区域特征进行自适应动态加权，得到相应的高频和低频融合结果；最后，基于一致性准则由高低频融合结果得到最终的融合结果，通过对SAR和TM图像的融合实验，验证了该方法在信息量，边界保持及融合结果一致性方面具有显著优势。