Multi-Modal and Adaptive Robot Control through Hierarchical Quadratic Programming

Abstract This paper proposes a novel Hierarchical Quadratic Programming (HQP)-based framework that enables multi-tasking control under multiple Human-Robot Interaction (HRI) scenarios. The proposed theoretical formulation of the controller is inspired by real-world contact-rich scenarios, which currently constitute one of the main applicability limits of most theoretical controllers. Indeed, HRI can occur through different modalities, based on human's needs. The objective is to create a unique framework for various types of interaction, avoiding the necessity of switching between different control types that are typically tailored for a specific task. To achieve this, we firstly propose a HQP-based hybrid Cartesian/joint space impedance control formulation. Based on system's dynamics, this provides an adaptive compliance behaviour of the robot, while performing hierarchical motion control. This is validated through a series of experiments that show the accuracy of trajectory tracking and the variable compliance behaviour. We then consider the case in which the human needs to move the robot directly, by proposing a hybrid admittance/impedance hierarchical control, which is validated through several experiments in which the human easily moves the robot in the workspace via interaction. Next, we formulate a HQP-based force controller through which a specific interaction force can be maintained and lastly, we extend this to simultaneous force and trajectory tracking. Overall, we obtain a multi-purpose HQP-based control framework, that can switch continuously between interaction modes, enabling multiple hierarchical behaviours that can cover a wide spectrum of interaction types, essential for synergistic HRI.