把基本机制创造成一个具有自我激励，自我组织构架的机器人#中英文翻译#外文翻译匹配

毕业设计（论文）外文翻译 译文题目： 创造机器人：把基本机制创造成一个具有自我激励，自我组织构架的机器人 外文题目： Bringing up robot: F undamental mechanism For creating a self-motiv ated, self-organizing architecture 系 别 机电工程系 专 业 机械设计制造及其自动化 班 级 161002 学生姓名 柯昌军 学 号 103311 指导教师 邓修瑾 1 Bringing up robot: F undamental mechanisms for creating a self-motiv ated, self-organizing architecture In this paper we describe an intrinsic developmental algorithm designed to allow a mobile robot to incrementally progress through levels of increasingly sophisticated behavior. We believe that the core ingredients for such a developmental algorithm are abstractions, anticipations, and self-motivations. We propose a multi-level, cascaded discovery and control architecture that includes these core ingredients. Toward this proposal we explore two novel models: a governor for automatically regulating the training of a neural network; and a path-planning neural network driven by patterns of mental states which represent protogoals. 1 Introduction Most intelligent robot control systems begin with the goal of creating a robot to carry out humanissued tasks. While these tasks v ary in difficulty , they must, by their very nature, involve abstract concepts. For example, typical tasks might be: go to a specific location, identify an object, or pick up an object. Attempting to directly achieve the goal of carrying out human commands creates basic assumptions about the architectural design of a robot. We call this philosophy task-oriented design. Within the task-oriented design paradigm, there are two competing methodologies: top-down, and bottom-up. T op-down designers apply computational algorithms that can be carried out on the robots so as to accomplish a given task. There is a range of computational models employed in robotics: dead reckoning (e.g., using internal measures of space), sensor fusion, behavior fusion, and symbolic logic. Bottom-up designers again usually take the task to be performed by the robot as a pre-specified assumption. However, the control architecture of the robot is designed in a bottom-up fashion.Examples include subsumption architectures, supervised learning schemes, and evolutionary computation. We believe that a significant pitfall exists in both the top-down and bottom-up task-oriented robot design methodologies: inherent anthropomorphic bias. This bias refers to the design of prespecified robot tasks: traditional research in the design of intelligent robots has attempted to get robots to do the tasks a human can, and do it in 2 a human-centered manner. Historically , this methodology started out by imitating the physical actions of a child playing with blocks. A task was decomposed into a planning problem, and then, with a robot equipped with an arm and a gripper, the robot was asked to manipulate specic blocks. The inherent anthropomorphic bias existed by design, since the issue was to explore models of intelligent behavior. The pitfall in this approach is that the symbolic modeling of behavior is based on the capabilities of a human body and human concepts. Both capabilities may be inappropriate assumptions for the physical body and experiences of the robot. Furthermore, even if we could build a robot with a human-like body and senses, it is not clear that we can jump straight to the abstract task at hand. Many control issues need to be solved in order to have a robotic system carry out even the simplest of tasks. After a half-century of continued research, the artificial intelligence and robotics communities are still far from developing any type of general purpose intelligent system. Recently , a new approach called developmental robotics is being applied to the design of robot behaviors. In this approach, an artifact under the control of an intrinsic developmental algorithm discovers capabilities through autonomous real-time interactions with its environment using its own sensors and effectors. That is, given a physical robot or an artifact, behaviors (as well as mental capabilities) are gr own using a developmental algorithm. The kinds of behaviors and mental capabilities exhibited are not explicitly specified. The focus is mainly on the intrinsic developmental algorithm and the computational models that allow an artifact to grow. A developmental approach to robotics is partly an attempt to eliminate anthropomorphic bias.By exploring the nature of development, the robot is essentially freed from the task of achieving a pre-specified goal. As long as the intrinsic developmental algorithm demonstrates growing behavior there is no need to pre-specify any particular task for the robot to perform. Indeed, it is the goal of developmental robotics to explore the range of tasks that can be learned (or grown) by a robot, given a specific developmental algorithm and a control architecture. This paper outlines our approach to a developmental robotics program and two experiments toward an implementation. 2 Overview of a Developmental Robotics Paradigm The ultimate goal of our developmental robotics program is to design a control architecture that could be installed within a robot so that when that robot is turned on 3 for the first time, it initiates an ongoing, autonomous developmental process. This process should be unsupervised, unscheduled, and task-less, and the architecture should work equally well on any robot platform|a fixed robot arm, a wheeled robot, or a legged robot. The intrinsic developmental process we are currently exploring contains three essential mechanisms: abstraction, anticipation, and self-motivation. In a realistic, dynamic environment, a robot is flooded with a constant stream of perceptual information. In order to use this information effectively for determining actions, a robot must have the ability to make abstractions so as to focus its attention on the most relevant features of the environment. Based on these abstractions, a robot must be able to anticipate how the environment will change over time, so as to go beyond simple reflexive behavior to purposeful behavior. Most importantly , the entire process is driven by internal motivations to push the system toward further abstractions and more complex anticipations. We believe that the developmental process should be employed in a hierarchical, bootstrapping manner, so as to result in the discovery of a range of increasingly sophisticated behaviors. That is, starting with a basic, built-in innate behavior, the robot exercises its sensors and motors, uses the mechanisms for abstraction and anticipation and discovers simple reflex behavior. A self-motivated control scheme employs these discoveries in order to supercede its innate behavior. This constitutes the first stage of the bootstrapping process. The same intrinsic developmental algorithm can be employed recursively in subsequent stages, using the knowledge discovered in previous stages. For example, a secondary stage may abstract sequences of behaviors and corresponding perceptual views. These behavior sequences, termed protoplans 12, can lead the robot through a series of views in the environment thus resulting in interesting places to visit. We will call these places protogoals. Here, the proto prefix implies a distinction between standard notions of plans and goals from the developmental ones used here.The same developmental process may be cascaded beyond this stage to result in discovery of actual goals and plans. The control scheme that is responsible for driving the robot at each stage uses the discovered abstractions and anticipations while being pushed by internal motivations. At the lowest level, the motivational model indicates to the system how comfortable it is in the given environment. If it is too comfortable, it becomes bored, and takes measures to move the robot into more interesting areas. Conversely , if the 4 environment is chaotic, it becomes over-excited and attempts to return to more stable and well known areas. These anthropomorphic terms will be described below in more technical terms. 5 创造机器人：把基本机制创造成一个具有自我激励，自我组织构架的机器人 在本文中，我们描述了一种具有内在发展算法的机器人，这种机器人能通过日益复杂的行为来提高自己。我们认为对于这样一个发展的算法它的核心成分是抽象的，随机应变的和自我动机的。于是我们提出了一个多层次的，级联的发现，和包括那些核心成分的控制结构。针对这样一个提议我们讨论了两种新的 模型：一个带自动调节神经网络的调节器和一个代表着“ protogoals”神经网络路径规划的模式驱动。 1 介绍 大多数机器人的控制系统开始于创建一个机器人来执行人们发出的目标任务。而这些任务都很困难，它们有本身的性质，是一些抽象的概念。例如，典型的任务可能是：去一个特定的位置，识别或者选择一个目标。试图直接实施人们的命令来创建关于机器人设计的基本假设。我们称这种理念为面向任务设计。 在面向任务设计理念里，有两套互相对立的设计方法：自顶向下设计和自底向上设计。自顶向上的设计算法可以让机器人完成指定的任务。 在机器人内部运用了一系列的计算模型：航位推算（例如，使用内部空间的措施），传感融合，行为融合，和符号逻辑。 自底而上的设计师往往让机器人执行一个预先指定好的假象任务。然而，对机器人的控制结构体系则是一个自底而上的设计方案。 我们认为在自顶而下和自底而上设计方案中都存在着一个重大的缺陷：内在的拟人化的偏见。这种偏见是指对预先指定的机器人任务的设计：智能机器人的传统研究已试图让机器人以人为中心做人们可以做的任务。从历史上看，这种方法开始于通过模仿孩子玩积木的肢体动作。目标任务被分解为一个规划方案，然后通 过配备一个臂和夹持器，机器人被用来处理特定的物体。内在的拟人化的偏见之所以能存在设计中，是因为这个问题是探讨智能行为的模型。这种方法的缺点是行为符号建模基于人体和人的观念能力之上。这 两个功能都将成为机器本身不恰当的假想。 此外，即使我们能制造一个与人类身体和感官一样的机器人，它也不会明确我们手上正要执行的任务。为了能有一个好的机器人来完成简单的任务，许多问题都要解决。经过一个半世纪的不断研究，人工智能和机器人领域仍然远未发展 6 任何类型的通用智能系统。 最近，一种新的方法称为发展机器人的方法被应用于机器人的行为设计中。在这种方法中，在一个具有固有发展能力算法的控制下，通过自主的实时互交，利用自身的传感器和环境效应来发展自己。就是说，给定一个物理或人造机器人，行为（