Abstract-It is known that for a large magnitude push a human or a humanoid robot must take a step to avoid a fall. Despite some scattered results, a principled approach towards "When and where to take a step" has not yet emerged.Towards this goal, we present methods for computing Capture Points and the Capture Region, the region on the ground where a humanoid must step to in order to come to a complete stop. The intersection between the Capture Region and the Base of Support determines which strategy the robot should adopt to successfully stop in a given situation.Computing the Capture Region for a humanoid, in general, is very difficult. However, with simple models of walking, computation of the Capture Region is simplified. We extend the wellknown Linear Inverted Pendulum Model to include a flywheel body and show how to compute exact solutions of the Capture Region for this model. Adding rotational inertia enables the humanoid to control its centroidal angular momentum, much like the way human beings do, significantly enlarging the Capture Region.We present simulations of a simple planar biped that can recover balance after a push by stepping to the Capture Region and using internal angular momentum. Ongoing work involves applying the solution from the simple model as an approximate solution to more complex simulations of bipedal walking, including a 3D biped with distributed mass.

This two-part paper discusses the analysis and control of legged locomotion in terms of N-step capturability: the ability of a legged system to come to a stop without falling by taking N or fewer steps. We consider this ability to be crucial to legged locomotion and a useful, yet not overly restrictive criterion for stability. Part 1 introduced the N-step capturability framework and showed how to obtain capture regions and control sequences for simplified gait models. In Part 2, we describe an algorithm that uses these results as approximations to control a humanoid robot. The main contributions of this part are (1) step location adjustment using the 1-step capture region, (2) novel instantaneous capture point control strategies, and 3) an experimental evaluation of the 1-step capturability margin. The presented algorithm was tested using M2V2, a 3D force-controlled bipedal robot with 12 actuated degrees of freedom in the legs, both in simulation and in physical experiments. The physical robot was able to recover from forward and sideways pushes of up to 21 Ns while balancing on one leg and stepping to regain balance. The simulated robot was able to recover from sideways pushes of up to 15 Ns while walking, and walked across randomly placed stepping stones.

This article is a summary of the experiences of the Florida Institute for Human & Machine Cognition (IHMC) team during the DARPA Robotics Challenge (DRC) Trials. The primary goal of the DRC is to develop robots capable of assisting humans in responding to natural and manmade disasters. The robots are expected to use standard tools and equipment to accomplish the mission. The DRC Trials consisted of eight different challenges that tested robot mobility, manipulation, and control under degraded communications and time constraints. Team IHMC competed using the Atlas humanoid robot made by Boston Dynamics. We competed against 16 international teams and placed second in the competition. This article discusses the challenges we faced in transitioning from simulation to hardware. It also discusses the lessons learned both during the competition and in the months of preparation leading up to it. The lessons address the value of reliable hardware and solid software practices. They also cover effective approaches to bipedal walking and designing for human‐robot teamwork. Lastly, the lessons present a philosophical discussion about choices related to designing robotic systems.

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.

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