With the ability to navigate natural and man-made environments and utilize standard human tools, humanoid robots have the potential to transform emergency response and disaster relief applications by serving as first responders in hazardous scenarios. Such applications will require major advances in humanoid control, enabling robots to traverse difficult, cluttered terrain with both speed and stability. To advance the state of the art, this dissertation presents a complete dynamic locomotion and whole-body control framework for compliant (torque-controlled) humanoids. We develop low-level, mid-level, and high-level controllers to enable low-impedance balancing and walking on compliant and uneven terrain.

For low-level control, we present a cascaded joint impedance controller for series elastic humanoids with parallel actuation. A distributed controller architecture is implemented using a dual-axis motor controller that computes desired actuator forces and motor currents using simple models of the joint mechanisms and series elastic actuators. An inner-loop force controller is developed using feedforward and PID control with a model-based disturbance observer, enabling naturally compliant behaviors with low joint impedance.

For mid-level control, we implement an optimization-based whole-body control strategy assuming a rigid body model of the robot. Joint torque setpoints are computed using an efficient quadratic program (QP) given desired joint accelerations, spatial accelerations, and momentum rates of change. Constraints on the centroidal dynamics, contact forces, and joint limits ensure admissibility of the optimized setpoints. Using this approach, we develop compliant standing and stepping behaviors based on simple feedback controllers.

For high-level control, we present a dynamic planning and control approach for humanoid locomotion using a novel time-varying extension of the Divergent Component of Motion (DCM). By varying the natural frequency of the DCM, we are able to achieve generic vertical center of mass (CoM) trajectories during walking. Complementary reverse-time integration and model predictive control (MPC) strategies are proposed to generate dynamically feasible DCM plans over a multi-step preview window, supporting locomotion on uneven terrain.

The proposed approach is validated through experimental results obtained using THOR, a 34 degree of freedom (DOF) series elastic humanoid. Rough terrain locomotion is demonstrated in simulation, and compliant locomotion and push recovery are demonstrated in hardware. We discuss practical considerations that led to a successful implementation on the THOR hardware platform and conclude with an application of the presented control framework for humanoid firefighting onboard the ex-USS Shadwell, a decommissioned Navy ship.