Lisa Meeden

An incremental approach to developing intelligent neural network controllers for robots

By beginning with simple reactive behaviors and gradually building up to more memory-dependent behaviors, it may be possible for connectionist systems to eventually achieve the level of planning. This paper focuses on an intermediate step in this incremental process, where the appropriate means of providing guidance to adapting controllers is explored. A local and a global method of reinforcement learning are contrasted-a special form of back-propagation and an evolutionary algorithm. These methods are applied to a neural network controller for a simple robot. A number of experiments are described where the presence of explicit goals and the immediacy of reinforcement are varied. These experiments reveal how various types of guidance can affect the final control behavior. The results show that the respective advantages and disadvantages of these two adaptation methods are complementary, suggesting that some hybrid of the two may be the most effective method. Concluding remarks discuss the next incremental steps toward more complex control behaviors.

Integrating robotics research with undergraduate education

Swarthmore College takes a two-pronged approach to undergraduate education by integrating educational goals with robotics research. First, it offers courses in artificial intelligence, computer vision and robotics. Both AI and computer vision serve as prerequisites for the robotics course. Second, it involves students in ongoing research projects as part of their undergraduate experience. To keep up with the wide-ranging, fast-moving robotics field, education must be adaptive and multidisciplinary. The authors describe two undergraduate group projects they conducted, one from 1998 at the University of North Dakota advised by Maxwell, and one from 1999 at Swarthmore advised by both authors. The impetus for these projects was the American Association for Artificial Intelligences (AAAIs) annual robot competition. These experiences led to the development of a new robotics course at Swarthmore, which they co-taught in the spring of 2000.

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.

TRENDS IN EVOLUTIONARY ROBOTICS

A review is given on the use of evolutionary techniques for the automatic design of adaptive robots. The focus is on methods which use neural networks and have been tested on actual physical robots. The chapter also examines the role of simulation and the use of domain knowledge in the evolutionary process. It concludes with some predictions about future directions in robotics.

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.

A support program for introductory CS courses that improves student performance and retains students from underrepresented groups

In line with institutions across the United States, the Computer Science Department at Swarthmore College has faced the challenge of maintaining a demographic composition of students that matches the student body as a whole. To combat this trend, our department has made a concerted effort to revamp our introductory course sequence to both attract and retain more women and minority students. The focus of this paper is the changes instituted in our Introduction to Computer Science course (i.e., CS1) intended for both majors and non-majors. In addition to changing the content of the course, we introduced a new student mentoring program that is managed by a full-time coordinator and consists of undergraduate students who have recently completed the course. This paper describes these efforts in detail, including the extension of these changes to our CS2 course and the associated costs required to maintain these efforts. We measure the impact of these changes by tracking student enrollment and performance over 13 academic years. We show that, unlike national trends, enrollment from underrepresented groups has increased dramatically over this time period. Additionally, we show that the student mentoring program has increased both performance and retention of students, particularly from underrepresented groups, at statistically significant levels.

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,

The AAAI 1999 Mobile Robot Competitions and Exhibitions

The Eighth Annual Mobile Robot Competition and Exhibition was held as part of the Sixteenth National Conference on Artificial Intelligence in Orlando, Florida, 18 to 22 July. The goals of these robot events are to foster the sharing of research and technology, allow research groups to showcase their achievements, encourage students to enter robotics and AI fields at both the undergraduate and graduate level, and increase awareness of the field. The 1999 events included two robot contests; a new, long-term robot challenge; an exhibition; and a National Botball Championship for high school teams sponsored by the KISS Institute. Each of these events is described in detail in this article.

Robot Learning a New Subfield? The Robolearn-96 Workshop

This article posits the idea of robot learning as a new subfield. The results of the Robolearn-96 Workshop provide evidence that learning in modern robotics is distinct from traditional machine learning. The article examines the role of robotics in the social and natural sciences and the potential impact of learning on robotics, generating both a continuum of research issues and a description of the divergent terminology, target domains, and standards of proof associated with robot learning. The article argues that although robot learning is a new subfield, there is significant potential for synergy with traditional machine learning if the differences in research cultures can be overcome.