Model-Based Control and Estimation of Humanoid Robots via Orthogonal Decomposition

Model-based control techniques, which use a model of robot dynamics to compute force/torque control commands, have a proven record for achieving accuracy and compliance in force-controllable robot manipulators. However, applying such methods to humanoid and legged systems has yet to happen due to challenges such as: 1) under-actuation inherent in these floating base systems, 2) dynamically changing contact states with potentially unknown contact forces, 3) and the difficulty of accurately modeling these high degree of freedom systems, especially with inadequate sensing. In this work, we present a relatively simple technique for fullbody model-based control and estimation of humanoid robot, using an orthogonal decomposition of rigid-body dynamics. Doing so simplifies the problem by reducing control and estimation to only those variables critical for the task. We present some of our recent evaluations of our approaches on the CarnegieMellon/Sarcos hydraulic force-controllable humanoid robot, engaging in dynamic tasks with contact state changes, such as standing up from a chair.

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