
AI Magazine Volume 27 Number 1 (2006) (© AAAI) Articles Components, Curriculum, and Community: Robots and Robotics in Undergraduate AI Education Zachary Dodds, Lloyd Greenwald, Ayanna Howard, Sheila Tejada, and Jerry Weinberg ■ This editorial introduction presents an of the engaging and active communities that overview of the robotic resources available to support robotic competitions and exhibitions. AI educators and provides context for the arti- These robot-based components, curricula, and cles in this special issue. We set the stage by ad- communities, we hope, broaden the resources dressing the trade-offs among a number of es- available to educators, as we all invite students tablished and emerging hardware and software to share our enthusiasm for AI. platforms, curricular topics, and robot contests used to motivate and teach undergraduate AI. Robot Platforms for AI Education Stuart Russell and Peter Norvig frame their widely used AI text through a paradigm of in- obot platforms have played a fundamen- telligent agents (Russell and Norvig 2003). tal role in the field of artificial intelli- Such an approach resonates with students, all Rgence (AI) for more than 30 years. Yet it of whom have deep experience with (and as) is only recently that physically embodied intelligent agents. Yet nearly all of that experi- agents have become a viable tool in the under- ence is with embodied intelligent agents, and graduate AI classroom. Examples of the flurry this familiarity makes robots a strong motiva- of activity in this area include competitions tor of AI. This embodiment contrasts with the and exhibitions, the growing options for low- majority of computer science subfields, in cost robot hardware and software, and a num- which computers interact with the physical ber of recent workshops and symposia. This world very differently than we do. What’s special issue of AI Magazine grew out of the more, for AI educators, robotic hardware is not 2004 AAAI spring symposium on Accessible, only a hook that can draw students to the field, Hands-on AI and Robotics Education. In this but a fundamental facet of the AI endeavor. article, we seek to showcase how robots have The challenge is to find a set of hardware and influenced both the curriculum and practice of software resources that serve both as motiva- teaching AI at the undergraduate level. tion and as tools to advance, not limit, the AI This survey article first overviews a number that students pursue in an academic course of of robot platforms and presents trade-offs in study. choosing among them. We then highlight the Today there exists a large and growing selec- variety of AI curricula supported by low-cost ro- tion of robotic platforms suitable for conveying botic platforms. We conclude with a summary and investigating fundamental AI topics. Fig- Copyright © 2006, American Association for Artificial Intelligence. All rights reserved. 0738-4602-2006 / $2.00 SPRING 2006 11 Articles ures 1, 2, and 3 summarize some of these re- Nintendo Game Boy Advance’s Xport Robot sources and their capabilities, with particular Controller (XRC) and related XBC (LeGrand et attention given to newer models and those al. 2005). Any device with a serial port or a con- widely employed at the undergraduate level. version to one can drive up to eight servo mo- These sensors are available from a number of tors using Pontech's SV 203, which is a small, retailers including HiTechnic Products and easily programmable controller (Bishop et al. Mindsensors.com for the RCX-compatible sen- 2004). Though less well established than the sors and Acroname for those sensors not specif- Handy Board and the Lego RCX controllers, all ically tailored to the RCX’s Lego interface. of these systems have been employed to teach Before discussing the platforms listed in fig- undergraduate AI and/or robotics. They typi- ure 1, it is worth mentioning a family of low- cally use a Lego or custom chassis and require cost robotics resources we have omitted: those some work to interface with Lego sensors. Oth- dedicated to teaching the electrical and me- er prebuilt controllers are also available, such as chanical engineering that underlies most con- Ridgesoft’s IntelliBrain, a more powerful, pro- temporary robotics, such as the basic stamp mi- prietary alternative to the Handy Board. crocontroller (Kuhnel and Zahnert 1997). One perceived disadvantage of robot kits is Undergraduate AI does not ignore the impact that the resulting platforms can provide soft- of such design decisions but instead focuses on ware support for only a low level of behavioral the computational challenges those decisions abstraction. Recent example curricula, such as create. In the context of AI education the hard- Greenwald’s (see his article in this issue of AI ware/software interface, that is, the ease with Magazine), mitigate these concerns: topics as which students can interact computationally computationally demanding and subtle as A* with a robot and investigate how their algo- search and Markov decision processes have run rithms behave, is a crucial criterion for evaluat- entirely on a Handy Board. Further, because it is ing robotic platforms. easy to download information from each of these platforms to a PC, all of these systems can Hardware and Software be used to collect data for off-board analysis, for A key advantage of the two most popular plat- example, learning the weights of a back propa- forms, Lego Mindstorms (or RCX brick) and the gation or Bayesian network (Greenwald and Handy Board, is the variety of ways in which Artz 2004). Susan Imberman demonstrated that students can program them. C-like languages the results of such analysis can return to the ro- and Java subsets are available for the Mind- bot for control, such as in a line-following task storms through the BricxCC and LeJOS based on neural network parameters learned off firmware upgrades. Both are open-source pro- board (Imberman 2004). On-board processing is jects with substantial deployment. Interactive needed only to the extent that sensed data must C is the default computational interface on the contribute to behavioral decisions during oper- Handy Board. A commercial Java implementa- ation. In practice, a more important disadvan- tion, RoboJDE, is available for the Handy Board tage of all of these platforms’ flexibility is that from RidgeSoft, LLC.1 These two platforms’ they are more difficult to support with simula- large user communities breed support for a tion software, as neither the robot morphology wide variety of interfaces: of particular note is nor its sensor suite is known a priori. the Lisp interface to the Lego RCX brick de- In contrast to robot kits, preassembled plat- scribed in detail later in this AI Magazine issue. forms offer additional capabilities and conve- Both Lego and Handy Board platforms provide nience at a higher cost. K-Team’s miniature ro- a microcontroller to which students attach a bot, the Khepera, provides options for a huge chassis, motors, and sensors. Their flexibility variety of sensors and actuators including color enables students’ hands-on investigation of the vision and a parallel-jawed gripper. Its small close relationship between physical agents’ form factor facilitates use in almost any space: form and function. Fred Martin’s text Robotic students have watched a Khepera execute their Explorations: An Introduction to Engineering programs from a desktop in a professor’s office Through Design (Martin 2000) is a popular and (Challinger 2005). The Khepera has proven it- natural starting point for Handy Board–based self within AI classes at a variety of schools courses. Several texts also build curricula (Harlan 2005, Kumar and Meeden 1998). K- around the RCX, such as Bagnall’s Core Lego Team’s newer and slightly larger robot, the He- Mindstorms Programming (Bagnall 2002). mission, also uses a ring of infrared (IR) sensors Other modular robotics options have grown for proximity sensing—at a cost an order of around off-the-shelf computational engines: magnitude lower than the Khepera. Not as ex- the Palm Pilot robot kit (Reshko, Mason, and pandable, it has also not yet received the same Nourbakhsh 2002; Avanzato 2004) and the scrutiny in educational or research settings. 12 AI MAGAZINE Articles Lego Mindstorms Popular because it works out-of-the-box, the yellow lego RCX brick has inspired curricula and freely $199 10x3x6 cm (w/o chassis) available software, such as is described in Klassner (2004), and Parsons and Sklar (2004). Sonar, rota- www.legomindstorms.com tion, and IR sensors can extend provided touch/ light inputs. Xport Robot Controller The Xport leverages the computation of a hand- held GameBoy (not included). KIPR offers a very $269 8x3x1cm (w/o chassis) capable vision-augmented system, the XBC (Le- Grand et al. 2005), that provides easily confi- www.charmedlabs.com gurable and very powerful region tracking. Handy Board Controller Although the price does not include sensors or a typically Lego chassis, the Handy Board supports touch, light, sonar, IR, compass, and $299 12x8x3 cm (w/o chassis) vision sensing. It is used in many AI courses (Imberman 2004, Danyluk 2004, Martin and handyboard.com Pantazopoulos 2004). The Palm Pilot stand-alone kit relies on Palm Pilot Robot Kit Acronameís B rainStem controller, three IR ranging sensors, and a handheld computer. $315 18 cm dia. x 6 cm Designed at Carnegie Mellon University (Reshko, Mason, and Nourbakhsh 2002) the PPRK has been www-2.cs.cmu.edu/~pprk employed for outreach beyond AI robotics (Avanzato 2004). Robix/Pontech The Robix enables many manipulator forms Manipulator via six servos and connective hardware. The Pon- tech SV203 ($60), an 8-motor serial controller, $550 varying dimensions can also control these servos (Crabbe 2004, Bishop et al.
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