Douglas S. Blank

Personalizing CS1 with robots

We have developed a CS1 curriculum that uses a robotics context to teach introductory programming [1]. Core to our approach is that each student has their own personal robot. Our robot and software have been specifically developed to support the needs of a CS1 curriculum. We frame traditional problems (robot control) in terms that are personal, relevant, and fun. Initial trial classes have shown that our approach is successful and adaptable.

Designing Personal Robots for Education: Hardware, Software, and Curriculum

An exciting new initiative at Georgia Tech and Bryn Mawr College is using personal robots both to motivate students and to serve as the primary programming platform for the Computer Science 1 curriculum. Here, the authors introduce the initiative and outline plans for the future.

Bringing up robot: Fundamental mechanisms for creating a self-motivated, self-organizing architecture

We propose an intrinsic developmental algorithm that is 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 describe a multilevel, cascaded discovery and control architecture that includes these core ingredients. As a first step toward implementing the proposed architecture, we explore two novel mechanisms: a governor for automatically regulating the training of a neural network and a path-planning neural network driven by patterns of “mental states” that represent protogoals.

Python robotics: an environment for exploring robotics beyond LEGOs

This paper describes Pyro, a robotics programming environment designed to allow inexperienced undergraduates to explore topics in advanced robotics. Pyro, which stands for Python Robotics, runs on a number of advanced robotics platforms. In addition, programs in Pyro can abstract away low-level details such that individual programs can work unchanged across very different robotics hardware. Results of using Pyro in an undergraduate course are discussed.

The Pyro Toolkit for AI and Robotics

This article introduces Pyro, an open-source Python robotics toolkit for exploring topics in AI and robotics. We present key abstractions that allow Pyro controllers to run unchanged on a variety of real and simulated robots. We demonstrate Pyros use in a set of curricular modules. We then describe how Pyro can provide a smooth transition for the student from symbolic agents to real-world robots, which significantly reduces the cost of learning to use robots. Finally we show how Pyro has been successfully integrated into existing Al and robotics courses.

Games, robots, and robot games: complementary contexts for introductory computing education

Using games to teach introductory computing courses provides another context with which to exploring the possible attraction, retention, and education of a new generation of computer science (CS) students. At Bryn Mawr College, we have been actively exploring these contexts and have identified four that have great promise for use in teaching introductory computing courses: visualization, multimedia, robotics, and, most recently, games. We are currently using and analysing robots and have some preliminary results. We believe that much of what we have learned in using robots in the classroom can be applied to the other contexts, especially gaming. In addition, many aspects of gaming can also be used in an introductory course using robots. This paper will explore robotics, gaming, their interactions, and provide suggestions on how best to proceed in making the most out of games in the classroom.

A music context for teaching introductory computing

We describe myro.chuck, a Python module for controlling music synthesis, and its applications to teaching introductory computer science. The module was built within the Myro framework using the ChucK programming language, and was used in an introductory computer science course combining robots, graphics and music. The results supported the value of music in engaging students and broadening their view of computer science.

Calico: a multi-programming-language, multi-context framework designed for computer science education

The Calico project is a multi-language, multi-context programming framework and learning environment for computing education. This environment is designed to support several interoperable programming languages (including Python, Scheme, and a visual programming language), a variety of pedagogical contexts (including scientific visualization, robotics, and art), and an assortment of physical devices (including different educational robotics platforms and a variety of physical sensors). In addition, the environment is designed to support collaboration and modern, interactive learning. In this paper we describe the Calico project, its design and goals, our prototype system, and its current use.

Introduction to developmental robotics

Developmental robotics is a broad, new discipline that lies at the intersections of psychology, biology, artificial intelligence (AI) and robotics. This new field was inspired by the fact that most complex and intelligent biological organisms (as opposed to artificial ones) undergo an extended period of development before reaching their adult form and adult abilities. This new rubric captures the essential features of many related, previous research agendas, including embodied cognition, evolutionary robotics and machine learning. Although developmental robotics combines many of these previous efforts, it also has fundamental differences that separate it in a number of interesting ways. To appreciate these differences, it is useful to reflect on the history of robotics and AI. Since the inception of AI in the 1950s, its practitioners have been striving to create intelligent machines. There have been some notable successes in restricted domains, such as game playing. However, the vision of creating general-purpose, human-like intelligence has not yet been achieved. To date, there have been three primary approaches to trying to create intelligent robots: direct programming; supervised machine learning; and evolutionary adaptation (Weng et al. 2001). In direct programming, a human engineer analyses the problem domain, determines a solution and then implements the solution in a program. Here the intelligence resides solely in the human engineer, the robot is merely acting out the pre-programmed commands. Robots created by direct programming tend to be brittle and fail in new situations not anticipated by the human engineer. In supervised learning, a human engineer creates a series of training situations describing how the robot should respond to particular sensory inputs. The robot learns to mimic the training data and typically makes useful generalizations that apply to novel situations that were never seen during training. This is an improvement over direct programming in that the robot, rather than the human engineer, determines how to solve the problem, and the robot can go beyond what it was initially exposed to, leading to more robust behaviour. However,

Patterns of Curriculum Design

We present a perspective on the design of a curriculum for a new computer science program at a women’s liberal arts college. The design incorporates lessons learned at the college in its successful implementation of other academic programs, incorporation of best practices in curriculum design at other colleges, results from studies performed on various computer science programs, and a significant number of our own ideas. Several observations and design decisions are presented as curriculum design patterns. The goal of making the design patterns explicit is to encourage a discussion on curriculum design that goes beyond the identification of core knowledge areas and courses.