MPC-QP-Based Control Framework for Compliant Behavior of Humanoid Robots in Physical Collaboration with Humans

We present a control framework specifically for physical human-humanoid collaboration involving the transportation and manipulation of heavy objects. Using this framework, the humanoid can exhibit desired levels of compliance with the object to be co-transported. This desired compliance is achieved through an admittance model. A Model Predictive Control (MPC) problem, based on a novel Interaction Linear Inverted Pendulum (I-LIP) model, generates footstep patterns that facilitate this desired compliant behavior while keeping the robot stable. Subsequently, we have an object-informed low-level quadratic program (QP) that sends control input to realize the high-level plans on the robot. The stiffness parameters of the I-LIP are modulated in real time for better compliance tracking performance of the robot. We verify all the results through simulation on the humanoid platform, the Digit, showing the prowess of the framework in collaboratively transporting heavy objects with a human.