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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)
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 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 the(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 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)
To enable robust dynamic walking on the Atlas robot, we extend our previous work by adding a receding-horizon component. The new controller consists of three hierarchies: a center of mass (CoM) trajectory planner that follows a sequence of desired foot steps, a receding-horizon controller that optimizes the next foot placement to minimize future CoM(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 trajectories, and a low level controller that tracks those trajectories by solving a floating base full body inverse dynamics(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)
Mathew DeDonato, Velin Dimitrov, Ruixiang Du, Ryan Giovacchini, Kevin Knoedler, Xianchao Long, Felipe Polido, Michael A. Gennert, and Taşkın Padır Robotics Engineering Program, Worcester Polytechnic Institute, 100 Institute Road, Worcester, Massachusetts 01609 Siyuan Feng, Hirotaka Moriguchi, Eric Whitman, X. Xinjilefu, and Christopher G. Atkeson Robotics(More)