Training for Open-Ended Drilling through a Virtual Reality Simulation

Virtual Reality (VR) can support effective and scalable training of psychomotor skills in manufacturing. However, many industry training modules offer experiences that are close-ended and do not allow for human error. We aim to address this gap in VR training tools for psychomotor skills training by exploring an open-ended approach to the system design. We designed a VR training simulation prototype to perform open-ended practice of drilling using a 3-axis milling machine. The simulation employs near "end-to-end" instruction through a safety module, a setup and drilling tutorial, open-ended practice complete with warnings of mistakes and failures, and a function to assess the geometries and locations of drilled holes against an engineering drawing. We developed and conducted a user study within an undergraduate-level introductory fabrication course to investigate the impact of open-ended VR practice on learning outcomes. Study results reveal positive trends, with the VR group successfully completing the machining task of drilling at a higher rate (75% vs 64%), with fewer mistakes (1.75 vs 2.14 score), and in less time (17.67 mins vs 21.57 mins) compared to the control group. We discuss our findings and limitations and implications for the design of open-ended VR training systems for learning psychomotor skills.Comment: 10 pages, 4 figures, 9 table

Theoretical inquiry in computational creative thinking

Thesis: S.M., Massachusetts Institute of Technology, Department of Architecture, 2017.Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 97-99).The vision of this research is to propose a novel computational framework to study Creative Thinking. If we are to embed machines with creative thinking abilities, then we first need to study the evanescent nature of human creative thinking. Creative thinking is neither entirely random nor strictly logical, making it difficult to t its computation into structured logical models of thinking. Given this conundrum, how can we computationally study the process of thinking creatively? In this research, I first present the current scientific definitions of creative thinking. Through literary survey of cognitive, computational and design thinking frameworks, I identify the missing links between human creativity and AI models of creative thinking. I assert that creative thinking is result of two features of human intelligence, cognitive diversity and social interaction. Cognitive diversity or the ability to parse knowledge in dierent ways is a crucial aspect of creative thinking. Furthermore, social interaction between cognitively diverse individuals results in restructuring of thoughts leading to creativity and epiphanies (the aha moments). I posit that Shape Grammar, with its ability to fluidly restructure computation, can be used to study and demonstrate cognitive diversity and interaction. If we conceive thoughts as shapes and ideas as configurations of those shapes, then cognitive diversity can be described as rule-based computation on shapes to generate those configurations; and interaction as the exchange of rules between cognitive diverse entities (humans or machines). The contributions of this research are threefold. First, I present a literature review of current frameworks, and identify the two gaps between machine and human creativity. Secondly, I demonstrate how shape grammar can ll those gaps of cognitive diversity and interaction. Thirdly, I propose thought-shape framework that adapts principles of shape grammar for computational creative thinking.by Dishita Girish Turakhia.S.M

Can Physical Tools that Adapt their Shape based on a Learner’s Performance Help in Motor Skill Training?

© 2021 Owner/Author. Adaptive tools that can change their shape to support users with motor tasks have been used in a variety of applications, such as to improve ergonomics and support muscle memory. In this paper, we investigate whether shape-adapting tools can also help in motor skill training. In contrast to static training tools that maintain task difficulty at a fixed level during training, shape-adapting tools can vary task difficulty and thus keep learners' training at the optimal challenge point, where the task is neither too easy, nor too difficult. To investigate whether shape adaptation helps in motor skill training, we built a study prototype in the form of an adaptive basketball stand that works in three conditions: (1) static, (2) manually adaptive, and (3) auto-adaptive. For the auto-adaptive condition, the tool adapts to train learners at the optimal challenge point where the task is neither too easy nor too difficult. Results from our two user studies show that training in the auto-adaptive condition leads to statistically significant learning gains when compared to the static (F1, 11 = 1.856, p < 0.05) and manually adaptive conditions (F1, 11 = 2.386, p < 0.05)