The Michigan Robotics Undergraduate Curriculum: Defining the Discipline
of Robotics for Equity and Excellence
- URL: http://arxiv.org/abs/2308.06905v1
- Date: Mon, 14 Aug 2023 02:53:20 GMT
- Title: The Michigan Robotics Undergraduate Curriculum: Defining the Discipline
of Robotics for Equity and Excellence
- Authors: Odest Chadwicke Jenkins, Jessy Grizzle, Ella Atkins, Leia Stirling,
Elliott Rouse, Mark Guzdial, Damen Provost, Kimberly Mann, and Joanna
Millunchick
- Abstract summary: The Robotics Major at the University of Michigan was launched in the 2022-23 academic year.
This document provides our original curricular proposal for the Robotics Undergraduate Program at the University of Michigan.
- Score: 6.279487567339418
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Robotics Major at the University of Michigan was successfully launched in
the 2022-23 academic year as an innovative step forward to better serve
students, our communities, and our society. Building on our guiding principle
of "Robotics with Respect" and our larger Robotics Pathways model, the Michigan
Robotics Major was designed to define robotics as a true academic discipline
with both equity and excellence as our highest priorities. Understanding that
talent is equally distributed but opportunity is not, the Michigan Robotics
Major has embraced an adaptable curriculum that is accessible through a
diversity of student pathways and enables successful and sustained career-long
participation in robotics, AI, and automation professions. The results after
our planning efforts (2019-22) and first academic year (2022-23) have been
highly encouraging: more than 100 students declared Robotics as their major,
completion of the Robotics major by our first two graduates, soaring
enrollments in our Robotics classes, thriving partnerships with Historically
Black Colleges and Universities. This document provides our original curricular
proposal for the Robotics Undergraduate Program at the University of Michigan,
submitted to the Michigan Association of State Universities in April 2022 and
approved in June 2022. The dissemination of our program design is in the spirit
of continued growth for higher education towards realizing equity and
excellence.
The most recent version of this document is also available on Google Docs
through this link: https://ocj.me/robotics_major
Related papers
- $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge.
We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
arXiv Detail & Related papers (2024-10-31T17:22:30Z) - Forecasting Success of Computer Science Professors and Students Based on Their Academic and Personal Backgrounds [0.0]
We analyze the influence of students' previous universities on their chances of being accepted to prestigious North American universities.
Our findings demonstrate that the ranking of their prior universities is a significant factor in achieving their goals.
arXiv Detail & Related papers (2023-11-04T18:30:24Z) - RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
arXiv Detail & Related papers (2023-11-02T17:59:21Z) - WIP: Development of a Student-Centered Personalized Learning Framework
to Advance Undergraduate Robotics Education [3.4359491310368786]
The study of robotics at the college level represents a wide range of interests, experiences, and aims.
This paper presents a work-in-progress on a learn-ing system that will provide robotics students with a personalized learning environment.
arXiv Detail & Related papers (2023-09-10T20:00:25Z) - Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement
Learning [54.636562516974884]
In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on.
In this work, we propose MEDAL++, a novel design for self-improving robotic systems.
The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
arXiv Detail & Related papers (2023-03-02T18:51:38Z) - GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots [87.32145104894754]
We introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots.
Our framework synthesizes general-purpose locomotion controllers that can be deployed on a large variety of quadrupedal robots.
We show that our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots.
arXiv Detail & Related papers (2022-09-12T15:14:32Z) - REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy
Transfer [57.045140028275036]
We consider the problem of transferring a policy across two different robots with significantly different parameters such as kinematics and morphology.
Existing approaches that train a new policy by matching the action or state transition distribution, including imitation learning methods, fail due to optimal action and/or state distribution being mismatched in different robots.
We propose a novel method named $REvolveR$ of using continuous evolutionary models for robotic policy transfer implemented in a physics simulator.
arXiv Detail & Related papers (2022-02-10T18:50:25Z) - Teaching Continuity in Robotics Labs in the Age of Covid and Beyond [0.0]
This paper argues that training of future Roboticists and Robotics Engineers in Computer Science departments, requires the extensive direct work with real robots.
This is exactly the problem that Robotics Labs encountered in early 2020, at the start of the Covid pandemic.
The exciting insight in the conclusion is that the work that was encouraged and triggered by a pandemic seems to have very positive longer-term benefits.
arXiv Detail & Related papers (2021-05-18T21:38:26Z) - The Road to a Successful HRI: AI, Trust and ethicS-TRAITS [65.60507052509406]
The aim of this workshop is to give researchers from academia and industry the possibility to discuss the inter-and multi-disciplinary nature of the relationships between people and robots.
arXiv Detail & Related papers (2021-03-23T16:52:12Z) - Robotics Enabling the Workforce [5.0555627833288]
We need to invest in basic research, technology development, K-16 education, and lifelong learning.
In order to make the U.S. a leader in robotics, we need to invest in basic research, technology development, K-16 education, and lifelong learning.
arXiv Detail & Related papers (2020-12-16T23:05:10Z) - Learning Locomotion Skills in Evolvable Robots [10.167123492952694]
We introduce a controller architecture and a generic learning method to allow a modular robot with an arbitrary shape to learn to walk towards a target and follow this target if it moves.
Our approach is validated on three robots, a spider, a gecko, and their offspring, in three real-world scenarios.
arXiv Detail & Related papers (2020-10-19T14:01:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.