Team IHMC’s Lessons Learned from the DARPA Robotics Challenge: Finding Data in the Rubble
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.
We would like to thank DARPA for sponsoring the Robotics Challenge and encouraging the advancement of robotics capabilities. We would also like to thank DARPA for the funding provided to IHMC to compete in the competition. We also thank Boston Dynamics for providing Atlas, which has been a solid and reliable robotic platform. Lastly, we would like to acknowledge our sponsors Atlassian and Amazon. Atlassian also provided an embedded engineer to ensure our agile practices were effectively applied using their Atlassian software tools.