For autonomous systems such as unmanned aerial vehicles tosuccessfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. In order to make use of diverse reasoning modules in such systems, issues ofintegration such as sensor data flow and information flow between such modules has to be taken into account. The DyKnow framework is a tool with a formal basis that pragmatically deals with many of the architectural issues which arise in such systems. This includes a systematic stream-based method for handling the sense-reasoning gap,caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many high-level reasoning modules. DyKnow has proven to be quite robust and widely applicable to different aspects of hybrid software architectures forrobotics. In this paper, we describe the DyKnow framework and show how it is integrated and used in unmanned aerial vehicle systems developed in our group. In particular, we focus on issues pertaining to the sense-reasoning gap and the symbol grounding problem and the use of DyKnow as a means of generating semantic structures representing situational awareness for such systems. We also discuss the use of DyKnow in the context of automated planning, in particular execution monitoring.

With the introduction of low-cost wireless communication many new applications have been made possible; applications where systems can collaboratively learn and get wiser without human supervision. One potential application is automated monitoring for fault isolation in mobile mechatronic systems such as commercial vehicles. The paper proposes an agent design that is based on uploading software agents to a fleet of mechatronic systems. Each agent searches for interesting state representations of a system and reports them to a central server application. The states from the fleet of systems can then be used to form a consensus from which it can be possible to detect deviations and even locating a fault.

Until recent years, the development of real-world humanoid robotics applications has been hampered by a lack of available mobile computational power. Unlike wheeled platforms, which can reasonably easily be expected to carry a payload of computers and batteries, humanoid robots couple a need for complex control over many degrees of freedom with a form where any significant payload complicates the balancing and control problem itself. In the last few years, however, an significant number of options for embedded processing suitable for humanoid robots have appeared (e.g. miniaturized motherboards such as beagle boards), along with ever-smaller and more powerful battery technology. Part of the drive for these embedded hardware breakthroughs has been the increasing demand by consumers for more sophisticated mobile phone applications, and these modern devices now supply much in the way of sensor technology that is also potentially of use to roboticists (e.g. accelerometers, cameras, GPS). In this paper, we explore the use of modern mobile phones as a vehicle for the sophisticated AI necessary for autonomous humanoid robots.

There is little doubt that the use of robots in introductory classes is an effective way to spark an initial interest in Computer Science and recruit students into our classes, and subsequently recruit some of them as Computer Science majors. But when the semester is over, the vast majority of our students are unlikely to see robots in the classroom again until they take advanced courses in AI or Robotics. It is time for those of us who are proponents of the use of robots in Introductory Computer Science to start thinking seriously about how we are using robots in our classes, and how to sustain the interest and enthusiasm of our students as they move on to more traditional courses. While the focus of this paper is on the use of robots in Introductory Computer Science courses, my goal is to initiate a more general discussion on the use of any sort of cool new technology (tangible or not) into both undergraduate and K-12 education. These technologies successfully attract students to study subjects that we ourselves are deeply engaged in. But we need to discuss as a community what happens when our individual classes conclude and the rest of their studies commence.

This paper reports on the Design Compass, a classroom tool for helping students record and reflect on their design process as they work on and complete a design challenge. The Design Compass software provides an interface where students can identify and record the various design steps they used while performing them, and add digital notes and pictures to document their work. In the Design Log view, students can review steps taken, and print the record of work done, which can be shared and discussed with their instructor or classmates. The paper describes the concepts underlying the creation of the Design Compass, its features as a metacognitive tool and how it works, and provides scenarios of its use as a teaching and assessment tool with eighth-grade technology education students, and in teacher professional development workshops.

This paper introduces the IRIS mobile robot project. IRIS is a largely student designed and implemented mobile robot platform created to provide a mechanism for classroom explorations of topics in artificial intelligence, cognitive science, and robotics. It has been designed to be used by students from middle school through college.

Evaluating new educational programs and tools, especially those targeted at difficult-to-assess learning goals can be quite challenging due to the small number of participants typically engaged with pilot programs. The focus of the evaluation, then, should be on collecting rich data from each participant about their experience in the workshop and their progress towards meeting the workshop’s learning goals. We present a novel evaluation technique, the debugging task, that seeks to assess at post-workshop a participant’s independent ability to use the tools, skills, and materials of the workshop. The technique is presented in the context of Robot Diaries, a program to develop a robotics design activity centered on crafts materials and expressiveness, and targeted to middle school girls. The paper discusses the rationale for the debugging task, its implementation, and the results and analyses of girls completing the task.

We present the design and deployment of a physical computing platform developed for a crossdisciplinary introduction to biology and computer science. Using the accessible Arduino interface as its foundation, students instantiate increasingly nuanced physical interactions with the environment. Biological and computational ideas receive equal attention through three layered projects that span from circuit design through the co-evolution of predator-prey robot behaviors. The low-overhead platform presented here scales to support sophisticated projects at surprisingly modest time-and-money costs

For the past several years, we have been using robots in our introductory computer science course. Although this has been challenging for many reasons, it has also been very rewarding on a number of fronts, both for the students and for us. However, in order for this to occur, we had to adapt to what we perceived as “chaotic code.” In this paper we describe lessons learned by watching what the students do, where they have trouble, and what they enjoy. Further, we discuss what the implications of focusing on creativity has had on teaching and assessment.

How is shape represented during spatial tasks such as mental rotation? This research investigated the format of mental representations of 3-D shapes during mental rotation. Specifically, we tested the extent to which visual information, such as color, is represented during mental rotation using methods ranging from reaction time studies, verbal protocol analysis, and eyetracking. Another set of studies examined whether people use piecemeal or holistic strategies to rotate complex objects. Results show that individuals with good rotation ability do not represent color during mental rotation and rotate whole shapes; whereas poor rotators do represent color and rotate individual pieces of the shape using piecemeal strategies. This work contributes to theories about cognitive shape processing by showing that different information processing strategies may be one cause of individual differences in mentally rotation performance.