Disney Research Pittsburgh

Entertainment robots in theme park environments typically do not allow for physical interaction and contact with guests. However, catching and throwing back objects is one form of physical engagement that still maintains a safe distance between the robot and participants. Using a theme park type animatronic humanoid robot, we developed a test bed for a throwing and catching game scenario.

This paper presents methods and experimental results regarding the identification of kinematic and dynamic parameters of force-controlled biped humanoid robots. We first describe a kinematic calibration method to estimate joint angle sensor offsets. The method is practical in the sense that it only uses joint angle and link orientation sensors, which most humanoid robots are equipped with.

This paper presents a tracking controller considering contact force constraints for floating-base humanoid robots.

In this paper, we improve the controller to address the issues related to root position/orientation estimation, model uncertainties, and the difference between expected and actual contact forces.

This paper presents a method for mapping captured human motion with stepping to a humanoid model, considering the current state and the controller behavior.

This paper presents a control framework for humanoid robots that uses all joints simultaneously to track motion capture data and maintain balance.

This project investigates the optimization of, and presents a control framework for, a biped robot to maintain balance and walk on a rolling ball.

This paper presents a control framework for a biped robot to maintain balance and walk on a rolling ball. The control framework consists of two primary components: a balance controller and a footstep planner.

Point-based targets, such as checkerboards, are often not practical for outdoor camera calibration, as cameras are usually at significant heights requiring extremely large calibration patterns on the ground.

Marker-less motion capture is a challenging problem, particularly when monocular video is all that is available. We estimate biped control from monocular video by implicitly recovering physically plausible three-dimensional motion of a subject along with a character model (controller) capable of replaying this motion in other environments and under physical perturbations.