Despite the recent achievements in stable dynamic walking for many humanoid robots, relatively little navigation autonomy has been achieved. In particular, the ability to autonomously select foot placement positions to avoid obstacles while walking is an important step towards improved navigation autonomy for humanoids. We present a footstep planner for the Honda ASIMO humanoid robot that plans a sequence of footstep positions to navigate toward a goal location while avoiding obstacles. The possible future foot placement positions are dependent on the current state of the robot. Using a finite set of state-dependent actions, we use an A* search to compute optimal sequences of footstep locations up to a time-limited planning horizon. We present experimental results demonstrating the robot navigating through both static and dynamic known environments that include obstacles moving on predictable trajectories.

Running is a complex dynamical task which places strict design requirements on both the physical components and software control systems of a robot. This paper explores some of those requirements and illustrates how a variable compliance actuation system can satisfy them. We present the design, analysis, simulation, and benchtop experimental validation of such an actuator system. We demonstrate, through simulation, the application of our prototype actuator to the problem of biped running.

Running is a complex dynamic task which places strict requirements on both the physical components and software control systems of a robot. This report explores some of those requirements and in particular explores how a variable compliance actuation system can satisfy many of them. We present the mechanical design and software control of such an actuator system. We analyze its performance through simulation and benchtop experimental validation of a prototype version. In conclusion we demonstrate, through simulation, the application of our prototype actuator to the problem of biped running.I

We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and "certificates" that ensure the output of an abstract high-level planner can be realized by lower layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of rough terrains. Other novel aspects of our past research efforts include a variety of pioneering inverse optimal control techniques as well as a system for planning using arbitrary pre-recorded robot behaviors.

Despite the stable walking capabilities of modern biped humanoid robots, their ability to autonomously and safely navigate obstacle-filled, unpredictable environments has so far been limited. We present an approach to autonomous humanoid walking that combines vision-based sensing with a footstep planner, allowing the robot to navigate toward a desired goal position while avoiding obstacles. An environment map including the robot, goal, and obstacle locations is built in real-time from vision. The footstep planner then computes an optimal sequence of footstep locations within a time-limited planning horizon. Footstep plans are reused and only partially recomputed as the environment changes during the walking sequence. In our experiments, combining real-time vision with plan reuse has allowed a Honda ASIMO humanoid robot to autonomously traverse dynamic environments containing unpredictably moving obstacles.