Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroom

Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students' computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware-enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course.Comment: 15 pages, 12 figures, 3 table

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

A New Method for Biomechanical Data Acquisition in Remote Laboratory Delivery During the COVID-19 Pandemic

Background: Delivery of a hands-on laboratory experience is a real challenge in the present pandemic environment. Many instructors tend to acquire and record experimental data, and then instruct their students to analyze such data to produce results and lab reports in an online course mode. Such a process considerably diminishes students’ motivation, engagement, and eagerness to explore further knowledge, as students appreciate more experimental data that they gather themselves. Such drawbacks are even more profound in a practical field such as biomechanics, where students need to feel the sense of kinematic and kinetic data of their own body motions and muscle forces.Purpose: The aim of this paper is to communicate with engineering educators facing challenges in the current pandemic time, a new teaching method that allows students to remotely gain a hands-on knowledge by applying the principles of mechanics on their body motions using a computer vision kinematics laboratory module that can be easily applied at home.Methods: In this research, students first capture their selected body motion with a webcam at home. They are provided a video tracking algorithm that calculate spatial locations of the body segment motion. Students then perform calculations to estimate further kinematic and kinetic data, plot them, and comment on their own estimated muscle forces. The students’ perceived workload and effort in completing the lab requirements can also be evaluated. The proposed computer vision approach is utilized to calculate the participants’ kinematic data in an interactive motion analysis problem.Results: As a work in progress study, initial results showed that 97% of the participating students successfully applied the computer vision-based kinematics module, supported by the generation and submission of full laboratory reports including critical data analysis of their body motion experience.Conclusions: The proposed computer vision experimental approach may enhance the learning experience of biomechanics students at home in such an isolating pandemic environment. The demonstrated methods can be applied to many teaching fields including biomechanics and robotics

Analysis and Observations from the First Amazon Picking Challenge

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge

Service-Learning Times : semester 1, 2018/19

Service-Learning (S-L) is a pedagogy that drives learning, innovation, contribution and transformation. It provides experiential learning opportunities for students by connecting academic knowledge to impactful community service with ongoing reflection during the process. The diverse and insightful S-L experience enables students to discover their potential, address the challenges faced by the community, as well as transform beneficiaries’ lives by providing support to collaborating partners. This booklet highlights the courses with S-L elements offered this semester. Students wishing to experience the best of S-L should plan and act quickly while places are available.https://commons.ln.edu.hk/sl_times/1002/thumbnail.jp

Toward future 'mixed reality' learning spaces for STEAM education

Digital technology is becoming more integrated and part of modern society. As this begins to happen, technologies including augmented reality, virtual reality, 3d printing and user supplied mobile devices (collectively referred to as mixed reality) are often being touted as likely to become more a part of the classroom and learning environment. In the discipline areas of STEAM education, experts are expected to be at the forefront of technology and how it might fit into their classroom. This is especially important because increasingly, educators are finding themselves surrounded by new learners that expect to be engaged with participatory, interactive, sensory-rich, experimental activities with greater opportunities for student input and creativity. This paper will explore learner and academic perspectives on mixed reality case studies in 3d spatial design (multimedia and architecture), paramedic science and information technology, through the use of existing data as well as additional one-on-one interviews around the use of mixed reality in the classroom. Results show that mixed reality can provide engagement, critical thinking and problem solving benefits for students in line with this new generation of learners, but also demonstrates that more work needs to be done to refine mixed reality solutions for the classroom

Pedagogical Possibilities for the N-Puzzle Problem

In this paper we present work on a project funded by the National Science Foundation with a goal of unifying the Artificial Intelligence (AI) course around the theme of machine learning. Our work involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Several hands-on laboratory projects that can be closely integrated into an introductory AI course have been developed. We present an overview of one of the projects and describe the associated curricular materials that have been developed. The project uses machine learning as a theme to unify core AI topics in the context of the N-puzzle game. Games provide a rich framework to introduce students to search fundamentals and other core AI concepts. The paper presents several pedagogical possibilities for the N-puzzle game, the rich challenge it offers, and summarizes our experiences using it

COVID-19 as a Magnifying Glass: Exploring the Importance of Relationships as Education Students Learn and Teach Robotics via Zoom

Ed+gineering, an NSF-funded program, adapted hands-on robotics instruction for online delivery in response to the COVID-19 pandemic. This qualitative multiple case study shares the experiences of participating education students in spring 2021 as they collaborated virtually with engineering students and fifth graders to engineer bioinspired robots in an afterschool technology club adapted to be virtual. The online context reduced the education students’ interactions with people other than the engineering students and fifth graders on their team and thus positioned COVID-19 as a metaphorical magnifying glass amplifying the critical role that these relationships played in influencing the project’s outcomes. Through analyzing short-answer reflections, the researchers observed patterns in the ways the education students’ interactions with their engineering and fifth-grade partners shaped their teaching self-efficacy and intention to integrate engineering and coding. Education students appeared to gain the most self-efficacy from feeling supported by, but not dependent upon, their engineering partners, and from adopting engineering-teaching roles. Satisfying interactions with fifth graders and successful production of functioning robots appeared to enhance education students’ intention to integrate engineering and coding into their future instruction. Education students reported gaining self-efficacy for both engineering and coding during the experience, but were more likely to report feeling confident about teaching engineering than teaching coding at the project’s end. Implications and lessons learned are shared, which may be particularly relevant for educators who prepare elementary education students to teach engineering in K-6 settings