This paper presents a high level overview of the work done by Team IHMC (Florida Institute for Human and Machine Cognition) to win the DARPA Virtual Robotics Challenge (VRC), held June 18-20 2013. The VRC consisted of a series of three tasks (driving a vehicle, walking over varied terrain, and manipulating a fire hose), to be completed in simulation using a model of the humanoid robot Atlas. Team IHMC was able to complete all of these challenges multiple times during the competition. The paper presents our approach, as well as a bird's-eye view of the major software components and their integration.

We present a method for humanoid robot walking on partial footholds such as small stepping stones and rocks with sharp surfaces. Our algorithm does not rely on prior knowledge of the foothold, but information about an expected foothold can be used to improve the stepping performance. After a step is taken, the robot explores the new contact surface by attempting to shift the center of pressure around the foot. The available foothold is inferred by the way in which the foot rotates about contact edges and/or by the achieved center of pressure locations on the foot during exploration. This estimated contact area is then used by a whole body momentum-based control algorithm. To walk and balance on partial footholds, we combine fast, dynamic stepping with the use of upper body angular momentum to regain balance. We applied this method to the Atlas humanoid designed by Boston Dynamics to walk over small contact surfaces, such as line and point contacts. We present experimental results and discuss performance limitations.

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To increase the speed of operation and reduce operator burden, humanoid robots must be able to function autonomously, even in complex, cluttered environments. For this to be possible, they must be able to quickly and efficiently compute desired footsteps to reach a goal. In this work, we present a new A* footstep planner that utilizes a planar region representation of the environment enable footstep planning over rough terrain. To increase the number of available footholds, we present an approach to allow the use of partial footholds during the planning process. The footstep plan solutions are then postprocessed to capture better solutions that lie between the lattice discretization of the footstep graph. We then demonstrate this planner over a variety of virtual and real world environments, including some that require partial footholds and rough terrain using the Atlas and Valkyrie humanoid robots.

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. C 2016 Wiley Periodicals, Inc.