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—We describe our full body humanoid control approach developed for the simulation phase of the DARPA Robotics Challenge (DRC), and the modifications made for the DRC Trials. We worked with the Boston Dynamics Atlas robot, which has 28 hydraulic actuators. Our approach was initially targeted at walking, and consisted of two levels of optimization, a high(More)
We propose a framework to use full-body dynamics for humanoid state estimation. The main idea is to decouple the full body state vector into several independent state vectors. Some decoupled state vectors can be estimated very efficiently with a steady state Kalman Filter. In a steady state Kalman Filter, state covariance is computed only once during(More)
We propose a framework for using full-body dynamics for humanoid state estimation. It is formulated as an optimization problem and solved with Quadratic Programming (QP). This formulation provides two main advantages over a nonlinear Kalman filter for dynamic state estimation. QP does not require the dynamic system to be written in the state space form, and(More)
— We present an optimization based real-time walking controller for a full size humanoid robot. The controller consists of two levels of optimization, a high level trajectory optimizer that reasons about center of mass and swing foot tra-jectories, and a low level controller that tracks those trajectories by solving a floating base full body inverse(More)
— We describe the design and hardware implementation of our walking and manipulation controllers that are based on a cascade of online optimizations. A virtual force acting at the robot's center of mass (CoM) is estimated and used to compensated for modeling errors of the CoM and unplanned external forces. The proposed controllers have been implemented on(More)
— One popular approach to controlling humanoid robots is through inverse kinematics (IK) through stiff joint position tracking. On the other hand, inverse dynamics (ID) based approaches have gained increasing acceptance by providing compliant motions and robustness to external perturbations. However, the performance of such methods is heavily dependent on(More)
— We describe Team WPI-CMU's approach to the DARPA Robotics Challenge (DRC), focusing on our strategy to avoid failures that required physical human intervention. We implemented safety features in our controller to detect potential catastrophic failures, stop the current behavior, and allow remote intervention by a human supervisor. Our safety methods and(More)
The DARPA Robotics Challenge (DRC) requires teams to integrate mobility, manipulation, and perception to accomplish several disaster-response tasks. We describe our hardware choices and software architecture, which enable human-in-the-loop control of a 28 degree-of-freedom ATLAS humanoid robot over a limited bandwidth link. We discuss our methods, results,(More)
The ParkourBot is an efficient and dynamic climbing robot. The robot comprises two springy legs connected to a body. Leg angle and spring tension are independently controlled. The robot climbs between two parallel walls by leaping from one wall to the other. During flight, the robot stores elastic energy in its springy legs and automatically releases the(More)
— We introduce a center of mass estimator, and its application in full body inverse dynamics control, fall detection and fall prevention for humanoid robots. We use the Linear Inverted Pendulum Model dynamics with an offset to predict the center of mass motion. This offset can be interpreted as a modelling error on the center of mass position, or an(More)