Team IHMC's Lessons Learned from the DARPA Robotics Challenge: Finding Data in the Rubble
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- 作者：Matthew Johnson ; Brandon Shrewsbury ; Sylvain Bertrand ; Duncan Calvert ; Tingfan Wu ; Daniel Duran ; Douglas Stephen ; Nathan Mertins ; John Carff ; William Rifenburgh ; Jesper Smith ; Chris Schmidt-Wetekam ; Davide Faconti ; Alex Graber-Tilton ; Nicolas Eyssette ; Tobias Meier ; Igor Kalkov ; Travis Craig ; Nick Payton ; Stephen McCrory ; Georg Wiedebach ; Brooke Layton ; Peter Neuhaus and Jerry Pratt
- 刊名：Journal of Field Robotics
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：34
- 期：2
- 页码：241-261
- 全文大小：2622K
- ISSN：1556-4967
文摘
This article presents a retrospective analysis of Team IHMC's experience throughout the DARPA Robotics Challenge (DRC), where we took first or second place overall in each of the three phases. As an extremely demanding challenge typical of DARPA, the DRC required rapid research and development to push the boundaries of robotics and set a new benchmark for complex robotic behavior. We present how we addressed each of the eight tasks of the DRC and review our performance in the Finals. While the ambitious competition schedule limited extensive experimentation, we will review the data we collected during the approximately three years of our participation. We discuss some of the significant lessons learned that contributed to our success in the DRC. These include hardware lessons, software lessons, and human-robot integration lessons. We describe refinements to the coactive design methodology that helped our designers connect human–machine interaction theory to both implementation and empirical data. This approach helped our team focus our limited resources on the issues most critical to success. In addition to helping readers understand our experiences in developing on a Boston Dynamics Atlas robot for the DRC, we hope this article will provide insights that apply more widely to robotics development and design of human–machine systems.