Posture control of robot manipulators with fuzzy voice commands using a fuzzy coach–player system

This paper presents a method of controlling robot manipulators with fuzzy voice commands. Recently, there has been some research on controlling robots using information-rich fuzzy voice commands such as 'go little slowly' and learning from such commands. However, the scope of all those works was limited to basic fuzzy voice motion commands. In this paper, we introduce a method of controlling the posture of a manipulator using complex fuzzy voice commands. A complex fuzzy voice command is composed of a set of fuzzy voice joint commands. Complex fuzzy voice commands can be used for complicated maneuvering of a manipulator, while fuzzy voice joint commands affect only a single joint. Once joint commands are learned, any complex command can be learned as a combination of some or all of them, so that, using the learned complex commands, a human user can control the manipulator in a complicated manner with natural language commands. Learning of complex commands is discussed in the framework of fuzzy coach–player model. The proposed idea is demonstrated with a PA-10 redundant manipulator.     

基于人类演示视频的机器人指令生成框架

为了提高机器人学习技能的能力,免除人工示教过程,本文基于对无特殊标记的人类演示视频的观察,提出了一种基于序列到序列模式的机器人指令自动生成框架。首先,使用Mask R-CNN（区域卷积神经网络）来缩小操作区域的范围,并采用双流I3D网络（膨胀3D卷积网络）从视频中提取光流特征和RGB特征;其次,引入双向LSTM（长短期记忆）网络从先前提取的特征中获取上下文信息;最后,使用自我注意力机制和全局注意力机制,学习视频帧序列和命令序列的关联性,序列到序列模型最终输出机器人的命令。在扩展后的MPII烹饪活动2数据集和IIT-V2C数据集上进行了大量的实验,与现有的方法进行比较,本文提出的方法在BLEU_4（0.705）和METEOR（0.462）等指标上达到目前最先进性能水平。结果表明,该方法能够从人类演示视频中学习操作任务。此外,本框架成功应用于Baxter机器人。     

Comprehension Grammars Generated from Machine Learning of Natural Languages

We are developing a theory of probabilistic language learning in the context of robotic instruction in elementary assembly actions. We describe the process of machine learning in terms of the various events that happen on a given trial, including the crucial association of words with internal representations of their meaning. Of central importance in learning is the generalization from utterances to grammatical forms. Our system derives a comprehension grammar for a superset of a natural language from pairs of verbal stimuli like Go to the screw! and corresponding internal representations of coerced actions. For the derivation of a grammar no knowledge of the language to be learned is assumed but only knowledge of an internal language.We present grammars for English, Chinese, and German generated from a finite sample of about 500 commands that are roughly equivalent across the three languages. All of the three grammars, which are context-free in form, accept an infinite set of commands in the given language.     

Experiments Toward a Mutual Adaptive Speech Interface That Adopts the Cognitive Features Humans Use for Communication and Induces and Exploits Users' Adaptations

Interactive agents such as pet robots or adaptive speech interface systems that require forming a mutual adaptation process with users should have two competences. One of these is recognizing reward information from users' expressed paralanguage information, and the other is informing the learning system about the users by means of that reward information. The purpose of this study was to clarify the specific contents of reward information and the actual mechanism of a learning system by observing how 2 persons could create a smooth speech communication, such as that between owners and their pets.

A communication experiment was conducted to observe how human participants create smooth communication through acquiring meaning from utterances in languages they did not understand. Then, based on experimental results, a meaning-acquisition model that considers the following 2 assumptions was constructed: (a) To achieve a mutual adaptive relationship with users, the model needs to induce users' adaptation and to exploit this induced adaptation to recognize the meanings of a user's speech sounds; and (b) to recognize users' utterances through trial-and-error interaction regardless of the language used, the model should focus on prosodic information in speech sounds, rather than on the phoneme information on which most past interface studies have focused.

The results confirmed that the proposed model could recognize the meanings of users' verbal commands by using participants' adaptations to the model for its meaning-acquisition process. However, this phenomenon was observed only when an experimenter gave the participants appropriate instructions equivalent to catchphrases that helped users learn how to use and interact intuitively with the model. Thus, this suggested the need for a subsequent study to discover how to induce the participants' adaptations or natural behaviors without giving these kinds of instructions.     

Auditory learning: a developmental method

Motivated by the human autonomous development process from infancy to adulthood, we have built a robot that develops its cognitive and behavioral skills through real-time interactions with the environment. We call such a robot a developmental robot. In this paper, we present the theory and the architecture to implement a developmental robot and discuss the related techniques that address an array of challenging technical issues. As an application, experimental results on a real robot, self-organizing, autonomous, incremental learner (SAIL), are presented with emphasis on its audition perception and audition-related action generation. In particular, the SAIL robot conducts the auditory learning from unsegmented and unlabeled speech streams without any prior knowledge about the auditory signals, such as the designated language or the phoneme models. Neither available before learning starts are the actions that the robot is expected to perform. SAIL learns the auditory commands and the desired actions from physical contacts with the environment including the trainers.     

Development of a diagnostic system using a testing-based approach for strengthening student prior knowledge

Students learn new instructions well by building on relevant prior knowledge, as it affects how instructors and students interact with the learning materials. Moreover, studies have found that good prior knowledge can enable students to attain better learning motivation, comprehension, and performance. This suggests it is important to assist students in obtaining the relevant prior knowledge, as this can enable them to engage meaningfully with the learning materials. Tests are often used to help instructors assess students’ prior knowledge. Nevertheless, conventional testing approaches usually assign only a score to each student, and this may mean that students are unable to realize their own individual weaknesses. To address this problem, instructors can diagnose the test results to provide more detailed information to each student, but this is obviously a time-consuming process. Therefore, this study proposes a testing-based diagnosis system to assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken. Furthermore, an experiment was conducted to evaluate the effectiveness of the proposed approach in an interdisciplinary course, since several studies have indicated that students learn more and better in such courses when applying relevant prior knowledge to what they are learning. The experimental results show that the developed system is able to effectively diagnose students’ prior knowledge and enhance their learning motivation and performance on an interdisciplinary course. In addition, two diagnostic evaluations were also conducted to assess whether the diagnoses given by the system were consistent with the decisions of experts. The results demonstrate that the proposed system can effectively assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken, since the diagnoses produced by the system were broadly consistent with those of experts.     

Commanding a humanoid to move objects in a multimodal language

This article describes a study on a humanoid robot that moves objects at the request of its users. The robot understands commands in a multimodal language which combines spoken messages and two types of hand gesture. All of ten novice users directed the robot using gestures when they were asked to spontaneously direct the robot to move objects after learning the language for a short period of time. The success rate of multimodal commands was over 90%, and the users completed their tasks without trouble. They thought that gestures were preferable to, and as easy as, verbal phrases to inform the robot of action parameters such as direction, angle, step, width, and height. The results of the study show that the language is fairly easy for nonexperts to learn, and can be made more effective for directing humanoids to move objects by making the language more sophisticated and improving our gesture detector.     

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.     

SignMT: An alternative language learning tool

Learning a second language is very difficult, especially, for the disabled; the disability may be a barrier to learn and to utilize information written in text form. We present the SignMT, Thai sign to Thai machine translation system, which is able to translate from Thai sign language into Thai text. In the translation process, SignMT takes into account the differences between Thai and Thai sign language in terms of both syntax and semantic to ensure the accuracy of translation. SignMT was designed to be not only an automatic interpreter but also a language learning tool. It provides meaning of each word in both text and image forms which is easy to understand by the deaf. The grammar information and the order of the sentence are presented in order to help the deaf in learning Thai, their second language. With SignMT, deaf students are less dependent on a teacher, have more freedom to experiment with their own language, and improve their knowledge and learning skill.     

Failure of motor learning for large initial errors

For certain complex motor tasks, humans may experience the frustration of a lack of improvement despite repeated practice. We investigate a computational basis for failure of motor learning when there is no prior information about the system to be controlled and when it is not practical to perform a thorough random exploration of the set of possible commands. In this case, if the desired movement has never yet been performed, then it may not be possible to learn the correct motor commands since there will be no appropriate training examples. We derive the mathematical basis for this phenomenon when the controller can be modeled as a linear combination of nonlinear basis functions trained using a gradient descent learning rule on the observed commands and their results. We show that there are two failure modes for which continued training examples will never lead to improvement in performance. We suggest that this may provide a model for the lack of improvement in human skills that can occur despite repeated practice of a complex task.     

Metacognitive learning in a fully complex-valued radial basis function neural network

Recent studies on human learning reveal that self-regulated learning in a metacognitive framework is the best strategy for efficient learning. As the machine learning algorithms are inspired by the principles of human learning, one needs to incorporate the concept of metacognition to develop efficient machine learning algorithms. In this letter we present a metacognitive learning framework that controls the learning process of a fully complex-valued radial basis function network and is referred to as a metacognitive fully complex-valued radial basis function (Mc-FCRBF) network. Mc-FCRBF has two components: a cognitive component containing the FC-RBF network and a metacognitive component, which regulates the learning process of FC-RBF. In every epoch, when a sample is presented to Mc-FCRBF, the metacognitive component decides what to learn, when to learn, and how to learn based on the knowledge acquired by the FC-RBF network and the new information contained in the sample. The Mc-FCRBF learning algorithm is described in detail, and both its approximation and classification abilities are evaluated using a set of benchmark and practical problems. Performance results indicate the superior approximation and classification performance of Mc-FCRBF compared to existing methods in the literature.     

Computer-assisted instruction in engineering education and training

A computer-based economy implies a computer-based education. Learning and teaching are optimized by the student, as well as for the professor, if instructional redundancy is captured for reuse. One of the fundamental problems, to be confronted in the construction of complex learning programs, is that of providing ever higher-level authoring languages. This paper shows how instructional reuse, as mediated by a random seeded crystal learning algorithm, can facilitate the evolution of complex learning frames. A natural language interface enables the sharing of large software projects across a team. Here, a knoqledge-based system acquires a capability to map natural language phrases onto object invocations. The natural language phrases are iteratively normalized through a transformative process, which utilizes band theory. A human-machine system is described, which entails the construction of a self-referential program editor. The presented concepts and arguments are codified with examples taken from information systems and flexible manufacturing. Learning is shown to be a consequence of randomization and reuse.     

Adaptive fuzzy command acquisition with reinforcement learning

Proposes a four-layered adaptive fuzzy command acquisition network (AFCAN) for adaptively acquiring fuzzy command via interactions with the user or environment. It can catch the intended information from a sentence (command) given in natural language with fuzzy predicates. The intended information includes a meaningful semantic action and the fuzzy linguistic information of that action. The proposed AFCAN has three important features. First, we can make no restrictions whatever on the fuzzy command input, which is used to specify the desired information, and the network requires no acoustic, prosodic, grammar, and syntactic structure, Second, the linguistic information of an action is learned adaptively and it is represented by fuzzy numbers based on α-level sets. Third, the network can learn during the course of performing the task. The AFCAN can perform off-line as well as online learning. For the off-line learning, the mutual-information (MI) supervised learning scheme and the fuzzy backpropagation (FBP) learning scheme are employed when the training data are available in advance. The former learning scheme is used to learn meaningful semantic actions and the latter learn linguistic information. The AFCAN can also perform online learning interactively when it is in use for fuzzy command acquisition. For the online learning, the MI-reinforcement learning scheme and the fuzzy reinforcement learning scheme are developed for the online learning of meaningful actions and linguistic information, respectively. An experimental system is constructed to illustrate the performance and applicability of the proposed AFCAN     

Modular fuzzy-neuro controller driven by spoken language commands.

We present a methodology of controlling machines using spoken language commands. The two major problems relating to the speech interfaces for machines, namely, the interpretation of words with fuzzy implications and the out-of-vocabulary (OOV) words in natural conversation, are investigated. The system proposed in this paper is designed to overcome the above two problems in controlling machines using spoken language commands. The present system consists of a hidden Markov model (HMM) based automatic speech recognizer (ASR), with a keyword spotting system to capture the machine sensitive words from the running utterances and a fuzzy-neural network (FNN) based controller to represent the words with fuzzy implications in spoken language commands. Significance of the words, i.e., the contextual meaning of the words according to the machine's current state, is introduced to the system to obtain more realistic output equivalent to users' desire. Modularity of the system is also considered to provide a generalization of the methodology for systems having heterogeneous functions without diminishing the performance of the system. The proposed system is experimentally tested by navigating a mobile robot in real time using spoken language commands.     

IMORL: Incremental Multiple-Object Recognition and Localization

This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an image. Unlike the conventional multiple-object learning algorithms, the proposed method can automatically and adaptively learn from continuous video streams over the entire learning life. This kind of incremental learning capability enables the proposed approach to accumulate experience and use such knowledge to benefit future learning and the decision making process. Furthermore, IMORL can effectively handle variations in the number of instances in each data chunk over the learning life. Another important aspect analyzed in this paper is the concept drifting issue. In multiple-object learning scenarios, it is a common phenomenon that new interesting objects may be introduced during the learning life. To handle this situation, IMORL uses an adaptive learning principle to autonomously adjust to such new information. The proposed approach is independent of the base learning models, such as decision tree, neural networks, support vector machines, and others, which provide the flexibility of using this method as a general learning methodology in multiple-object learning scenarios. In this paper, we use a neural network with a multilayer perceptron (MLP) structure as the base learning model and test the performance of this method in various video stream data sets. Simulation results show the effectiveness of this method.     

Dependence directed reasoning and learning in systems maintenancesupport

The maintenance of large information systems involves continuous modifications in response to evolving business conditions or changing user requirements. Based on evidence from a case study, it is shown that the system maintenance activity would benefit greatly if the process knowledge reflecting the teleology of a design could be captured and used in order to reason about he consequences of changing conditions or requirements, A formalism called REMAP (representation and maintenance of process knowledge) that accumulates design process knowledge to manage systems evolution is described. To accomplish this, REMAP acquires and maintains dependencies among the design decisions made during a prototyping process, and is able to learn general domain-specific design rules on which such dependencies are based. This knowledge cannot only be applied to prototype refinement and systems maintenance, but can also support the reuse of existing design or software fragments to construct similar ones using analogical reasoning techniques     

“Verba Volant Scripta Manent” a false axiom within virtual environments. A semi-automatic tool for retrieval of semantics understanding for speech-enabled VR applications

Traditional interaction with virtual environments (VE) via widgets or menus forces users to rigidly sequential interactions. Previous research has proved that the adoption of speech recognition (SR) allows more flexible and natural forms of interaction resembling the human-to-human communication pattern. This feature though requires programmers to compile some human supplied knowledge in the form grammars. These are then used at runtime to process spoken utterances into complete commands. Further speech recognition (SR) must be hard-coded into the application.This paper presents a completely automatic process to build a body of knowledge from the information embedded within the application source code. The programmer in fact embeds, throughout the coding process, a vast amount of semantic information. This research work exploits this semantic richness and it provides a self-configurable system, which automatically adapts its understanding of human commands according to the content and to the semantic information defined within the application's source code.     

一种MAS构件知识复用动态演化模型

为提高企业的知识利用效率,增强企业创新能力,针对企业现有知识和系统,提出将企业知识管理的业务逻辑与知识处理事务分开,建立了基于多智能体和构件知识的知识复用模型,设计了知识管理业务逻辑的规则模型和智能体的活动行为模型,讨论了基于多智能体的规则协调模式,有效地支持知识的动态复用和知识使用过程的动态重组,增强知识管理系统的分布式处理能力和规模可扩展能力.在分布式构件库系统中,智能体通过协作联合完成任务要求.每个智能体拥有自己的知识库,并且具备学习能力,能够更新其知识库以保持执行结果的有效性.     

一种基于自生成样本学习的奖赏塑形方法

强化学习通过从以往的决策反馈中学习,使Agent 做出正确的短期决策,以最大化其获得的累积奖赏值.以往研究发现,奖赏塑形方法通过提供简单、易学的奖赏替代函数(即奖赏塑性函数)来替换真实的环境奖赏,能够有效地提高强化学习性能.然而奖赏塑形函数通常是在领域知识或者最优策略示例的基础上建立的,均需要专家参与,代价高昂.研究是否可以在强化学习过程中自动地学习有效的奖赏塑形函数.通常,强化学习算法在学习过程中会采集大量样本.这些样本虽然有很多是失败的尝试,但对构造奖赏塑形函数可能提供有用信息.提出了针对奖赏塑形的新型最优策略不变条件,并在此基础上提出了RFPotential 方法,从自生成样本中学习奖赏塑形.在多个强化学习算法和问题上进行了实验,其结果表明,该方法可以加速强化学习过程.