Duckietown: An Innovative Way to Teach Autonomy
Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehicle autonomy class that experiments with teaching...
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Springer Cham
2017
|
| Online Access: | http://hdl.handle.net/1721.1/111059 https://orcid.org/0000-0003-2492-6660 https://orcid.org/0000-0003-2652-8017 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0002-8863-6550 |
| _version_ | 1826216502197485568 |
|---|---|
| author | Tani, Jacopo Paull, Liam Zuber, Maria Rus, Daniela L How, Jonathan P Leonard, John J Censi, Andrea |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Tani, Jacopo Paull, Liam Zuber, Maria Rus, Daniela L How, Jonathan P Leonard, John J Censi, Andrea |
| author_sort | Tani, Jacopo |
| collection | MIT |
| description | Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehicle autonomy class that experiments with teaching innovations in addition to leveraging modern educational theory for improving student learning. We provide a robot to every student, thanks to a minimalist platform design, to maximize active learning; and introduce a role-play aspect to increase team spirit, by modeling the entire class as a fictional start-up (Duckietown Engineering Co.). The course formulation leverages backward design by formalizing intended learning outcomes (ILOs) enabling students to appreciate the challenges of: (a) heterogeneous disciplines converging in the design of a minimal self-driving car, (b) integrating subsystems to create complex system behaviors, and (c) allocating constrained computational resources. Students learn how to assemble, program, test and operate a self-driving car (Duckiebot) in a model urban environment (Duckietown), as well as how to implement and document new features in the system. Traditional course assessment tools are complemented by a full scale demonstration to the general public. The “duckie” theme was chosen to give a gender-neutral, friendly identity to the robots so as to improve student involvement and outreach possibilities. All of the teaching materials and code is released online in the hope that other institutions will adopt the platform and continue to evolve and improve it, so to keep pace with the fast evolution of the field. |
| first_indexed | 2024-09-23T16:48:43Z |
| format | Article |
| id | mit-1721.1/111059 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:48:43Z |
| publishDate | 2017 |
| publisher | Springer Cham |
| record_format | dspace |
| spelling | mit-1721.1/1110592022-10-03T08:28:38Z Duckietown: An Innovative Way to Teach Autonomy Tani, Jacopo Paull, Liam Zuber, Maria Rus, Daniela L How, Jonathan P Leonard, John J Censi, Andrea Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Tani, Jacopo Paull, Liam Zuber, Maria Rus, Daniela L How, Jonathan P Leonard, John J Censi, Andrea Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehicle autonomy class that experiments with teaching innovations in addition to leveraging modern educational theory for improving student learning. We provide a robot to every student, thanks to a minimalist platform design, to maximize active learning; and introduce a role-play aspect to increase team spirit, by modeling the entire class as a fictional start-up (Duckietown Engineering Co.). The course formulation leverages backward design by formalizing intended learning outcomes (ILOs) enabling students to appreciate the challenges of: (a) heterogeneous disciplines converging in the design of a minimal self-driving car, (b) integrating subsystems to create complex system behaviors, and (c) allocating constrained computational resources. Students learn how to assemble, program, test and operate a self-driving car (Duckiebot) in a model urban environment (Duckietown), as well as how to implement and document new features in the system. Traditional course assessment tools are complemented by a full scale demonstration to the general public. The “duckie” theme was chosen to give a gender-neutral, friendly identity to the robots so as to improve student involvement and outreach possibilities. All of the teaching materials and code is released online in the hope that other institutions will adopt the platform and continue to evolve and improve it, so to keep pace with the fast evolution of the field. National Science Foundation (U.S.) (Award IIS #1318392) National Science Foundation (U.S.) (Award #1405259) 2017-08-29T17:41:34Z 2017-08-29T17:41:34Z 2017-08-29 Article http://purl.org/eprint/type/ConferencePaper 978-3-319-55552-2 978-3-319-55553-9 2194-5357 2194-5365 http://hdl.handle.net/1721.1/111059 Tani, Jacopo et al. “Duckietown: An Innovative Way to Teach Autonomy.” Alimisis D., Moro M. and Menegatti E., editors. Educational Robotics in the Makers Era. Edurobotics 2016. Advances in Intelligent Systems and Computing 560 (2017): 104–121 © Springer International Publishing AG 2017 https://orcid.org/0000-0003-2492-6660 https://orcid.org/0000-0003-2652-8017 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0002-8863-6550 en_US http://dx.doi.org/10.1007/978-3-319-55553-9_8 Advances in Intelligent Systems and Computing Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer Cham MIT Web Domain |
| spellingShingle | Tani, Jacopo Paull, Liam Zuber, Maria Rus, Daniela L How, Jonathan P Leonard, John J Censi, Andrea Duckietown: An Innovative Way to Teach Autonomy |
| title | Duckietown: An Innovative Way to Teach Autonomy |
| title_full | Duckietown: An Innovative Way to Teach Autonomy |
| title_fullStr | Duckietown: An Innovative Way to Teach Autonomy |
| title_full_unstemmed | Duckietown: An Innovative Way to Teach Autonomy |
| title_short | Duckietown: An Innovative Way to Teach Autonomy |
| title_sort | duckietown an innovative way to teach autonomy |
| url | http://hdl.handle.net/1721.1/111059 https://orcid.org/0000-0003-2492-6660 https://orcid.org/0000-0003-2652-8017 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0002-8863-6550 |
| work_keys_str_mv | AT tanijacopo duckietownaninnovativewaytoteachautonomy AT paullliam duckietownaninnovativewaytoteachautonomy AT zubermaria duckietownaninnovativewaytoteachautonomy AT rusdanielal duckietownaninnovativewaytoteachautonomy AT howjonathanp duckietownaninnovativewaytoteachautonomy AT leonardjohnj duckietownaninnovativewaytoteachautonomy AT censiandrea duckietownaninnovativewaytoteachautonomy |