QT
参考网站：http://www.trolltech.com

Qt是Trolltech公司（已被NOKIA收购） 的一个多平台的C++图形用户界面应用程序框架。它提供给应用程序开发者建立艺术级的图形用户界面所需的所用功能。Qt 是完全面向对象的很容易扩展，并且允许真正地组件编程。自从1996年早些时候，Qt进入商业领域，它已经成为全世界范围内数千种成功的应用程序的基础。 Qt也是流行的Linux桌面环境KDE 的基础，同时它还支持Windows、Macintosh、Unix/X11等多种平台。

EMACS
http://www.gnu.org/software/emacs/

Emacs是一种强大的文本编辑器，在程序员和其他以技术工作为主的计算机用户中广受欢迎。EMACS，即Editor MACroS（编辑器宏）的缩写，最初由Richard Stallman(理 查德·马修·斯托曼)于1975年在MIT协同Guy Steele共同完成。这一创意的灵感来源于TECMAC和TMACS，它们是由Guy Steele、Dave Moon、Richard Greenblatt、Charles Frankston等人编写的宏文本编辑器。自诞生以来，Emacs演化出了众多分支，其中使用最广泛的两种分别是：1984年由Richard Stallman发起并由他维护至今的GNU Emacs，以及1991年发起的XEmacs。XEmacs是GNU Emacs的分支，至今仍保持着相当的兼容性。它们都使用了Emacs Lisp这种有着极强扩展性的编程语言，从而实现了包括编程、编译乃至网络浏览等等功能的扩展。
　　在Unix文化里，Emacs是黑客们关于编辑器优劣之争的两大主角之一，它的对手是vi。

SVN
subversion(简称svn)是近年来崛起的版本管理工具，是cvs的接班人。目前，绝大多数开源软件都使用svn作为代码版本管理软件。
http://subversion.tigris.org/

"Doxygen is a documentation system for C++, IDL (Corba, Microsoft, and KDE-2 DCOP flavors) and C. 

It can help you in three ways: 

1. It can generate an on-line documentation browser (in HTML) and/or an off-line reference manual (in ) from a set of documented source files. There is also support for generating output in RTF (MS-Word), Postscript, hyperlinked PDF, compressed HTML, and Unix man pages. The documentation is extracted directly from the sources, which makes it much easier to keep the documentation consistent with the source code. 
   
2. Doxygen can be configured to extract the code structure from undocumented source files. This can be very useful to quickly find your way in large source distributions. The relations between the various elements are be visualized by means of include dependency graphs, inheritance diagrams, and collaboration diagrams, which are all generated automatically. 
   
3. You can even `abuse' doxygen for creating normal documentation."

if you want to know more about doxygen, please refer to http://www.stack.nl/~dimitri/doxygen/

introduction:
Human intelligence is acquired through a prolonged period of maturation and growth during which a single fertilized egg first turns into an embryo, then grows into a newborn baby, and eventually becomes an adult individual – which, typically before growing old and dying, reproduces. The processes underlying developmental changes are inherently robust and flexible as demonstrated by the amazing ability of biological organisms to devise adaptive strategies and solutions to cope with environmental changes and guarantee their survival. Because evolution has selected development as the process through which to realize some of the highest known forms of intelligence, it is reasonable to assume that development is mechanistically crucial to emulate such intelligence in machines and other human-made artifacts.

Aspects and Areas of Interest

Developmental robotics differs from traditional robotics and artificial intelligence in at least two crucial aspects. First, there is a strong emphasis on body structure and environment as causal elements in the emergence of organized behavior and cognition requiring their explicit inclusion in models of emergence and development of cognition (Asada et al., 2001; Blank et al., 2005; Lungarella et al., 2003; Weng et al., 2001; Zlatev and Balkenius, 2001). Although some researchers use simulated environments and computational models (Kuniyoshi and Sangawa, 2006; Mareschal et al., 2007; Westermann et al., 2006), more often developmental robots are embedded in the real world as physical analogues of real organisms (e.g. Arbib et al., 2007; Kozima and Nakagawa, 2007; Metta and Fitzpatrick, 2003; Pfeifer et al., 2007; Sporns, 2007; for examples). Second, the idea is to realize artificial cognitive systems not by simply programming them (e.g. to solve a specific task), but rather by initiating and maintaining a developmental process during which the systems interact with their physical environments (i.e. through their bodies, tools, or other artifacts), as well as with their social environments (i.e. with people, other robots, or simulated agents) – cognition, after all, is the result of a process of self-organization (spontaneous emergence of order) and co-development between a developing organism and its surrounding environment. Andy Clark uses the term “cognitive incrementalism” to denote the bootstrapping of intelligence, the rationale being that throughout life “you get indeed get full-blown, human cognition by gradually adding bells and whistles to basic strategies of relating to the present at hand” (Clark, 2001). In other words, incrementalism designates the process of starting with a minimal set of functions and building increasingly more functionality in a step by step manner on top of structures that are already present in the system.

The spectrum of developmental robotics research can be roughly segmented into four primary areas of interest. The borders of these categories are not as clearly defined as this classification may suggest and instances may exist that fall into two or more of these categories. We do hope, however, that the suggested grouping provides at least some order in the large spectrum of issues addressed by developmental roboticists.

• Socially oriented interaction: This category comprises research on robots that communicate or learn particular skills via social interaction with humans or with other robots. Examples include research on imitation learning, communication and language acquisition, attention sharing, turn-taking behavior, and social regulation (e.g. Breazeal and Scassellati, 2002; Dautenhahn, 2007; Fong et al., 2003; Steels, 2006).

• Non-social interaction: These studies are characterized by a direct and strong coupling between sensor and motor processes and the local environment (e.g. inanimate objects), but do not involve any interaction with other robots or humans. Examples are visually-guided grasping and manipulation, tool-use, perceptual categorization, and navigation (e.g. Fitzpatrick et al., 2006; Metta and Fitzpatrick, 2003; Nabeshima et al., 2006).

• Agent-centered sensorimotor control: In these studies, the goal is to investigate the exploration of bodily capabilities, changes of morphology (e.g. perceptual acuity, or strength of the effectors) and their effects on motor skill acquisition, self-supervised learning schemes not specifically linked to any functional goal, and models of emotion. Examples include self-exploration, categorization of motor patterns, motor babbling, and learning to walk or crawl (e.g. Demiris and Meltzoff, 2007; Kuniyoshi and Sangawa, 2006; Lungarella and Berthouze, 2002).　

• Mechanisms and principles: This category embraces research on mechanisms or processes thought to increase the adaptivity of a behaving system. Many examples exist: developmental and neural plasticity, mirror neurons, motivation, freezing and freeing of degrees of freedom, and synergies; research into the characterization of complexity and emergence, as well as the effects of adaptation and growth; practical work on body construction or development (e.g. Arbib et al., 2007; Blank et al., 2005; Lungarella and Sporns, 2006; Oudeyer et al., 2007; Pfeifer et al., 2007). Further work in this area of interest relates to design principles for developmental systems.

Challenge

The further success of developmental robotics will depend on the extent to which theorists and experimentalists will be able to identify universal principles spanning the multiple levels at which developmental systems operate. In what follows, we briefly indicate some of the “hot” issues that will need to be tackled in the future.

• Semiotics: It is necessary to address the issue of how developmental robots (and embodied agents in general) can give meaning to symbols and construct semiotic systems. A promising approach – explored under the label of “semiotic dynamics” – is that such semiotic systems and the associated information structure are not static, but are continuously invented and negotiated by groups of people or agents which use them for communication and information organization (Steels, 2006).

• Core knowledge: An organism cannot develop without some built-in ability. If all abilities are built in, however, the organism does not develop either. It will therefore be important to understand with what sort of core knowledge and explorative behaviors a developmental system has to be endowed so that it can begin developing novel skills on its own. One of the greatest challenges will be to identify what those core abilities are and how they interact during development in building basic skills (e.g. RobotCub Roadmap, 2007; Spelke, 2000).

• Core motives: It is necessary to conduct research on general capacities such as creativity, curiosity, motivations, action selection, and prediction (i.e. the ability to foresee consequence of actions). Ideally, no tasks should be pre-specified to the robot, which should only be provided with an internal abstract reward function, some core knowledge, and a set of basic motivational (or emotional) "drives" that could push it to continuously master new know-how and skills (Breazeal, 2003; Oudeyer et al., 2007; Lewis, 2000; RobotCub Roadmap, 2007; Velasquez, 2007).

• Self-exploration: Another important challenge is the one of continuous self-programming and self-modeling (e.g. Bongard et al., 2006). Control theory assumes that target values and statuses are initially provided by the system’s designer, whereas in biology, such targets are created and revised continuously by the system itself. Such spontaneous “self-determined evolution” or “autonomous development” is beyond the scope of current control theory and needs to be tackled in future research.

• Active learning: In a natural setting, no teacher can possibly provide a detailed learning signal and sufficient training data. Mechanisms will have to be created for the developing agent to collect relevant learning material on its own and for learning to take place in an “ecological context” (i.e. with respect to the environment). One significant future avenue will be to endow systems with the possibility to recognize progressively longer chains of cause and effect (Chater et al., 2006).

• Growth: As mentioned in the introduction, intelligence is acquired through a process of self-assembly, growth, and maturation. It will be important to study how physical growth, change of shape and body composition, as well as material properties of sensors and actuators affect and guide the emergence and development of cognition and action. This will allow connecting developmental robotics to computational developmental biology (Gomez and Eggenberger, 2007; Kumar and Bentley, 2003).

http://www-robotics.cs.umass.edu/Main/HomePage
http://www.cs.uu.nl/groups/IS/robotics/robotics.html
http://www.inl.gov/adaptiverobotics/humanoidrobotics/future.shtml
http://www.nrl.navy.mil/aic/iss/aas/CognitiveRobots.php
http://cogrob.ensta.fr/
http://eecs.vanderbilt.edu/cis/cishome.shtml
http://ni.www.techfak.uni-bielefeld.de/                                (I am here)
http://www.scholarpedia.org/article/Developmental_robotics
http://www.idsia.ch/~juergen/cogbotlab.html


...to be continue