Multi-Modal and Adaptive Robot Control through Hierarchical Quadratic Programming

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 controllers' formulations are inspired by real-world contact-rich scenarios, which currently constitute one of the main limitations in terms of widespread practical deployment. Indeed, HRI can occur through different modalities, based on human's needs. The objective is to create a unique framework for various types of possible interactions, avoiding the necessity of switching between different control architectures, which requires dealing with discontinuities. To achieve this, we firstly propose a HQP-based hybrid Cartesian/joint space impedance control formulation. Based on the robot's dynamics, this controller enables an adaptive compliance behaviour, while achieving hierarchical motion control. This is validated through a series of experiments that show the accuracy of trajectory tracking, which remains in the order of 10mm during fast motions thanks to the addition of the robot dynamics. Besides, the hybrid compliance behaviour allows to deviate from such accuracy when an interaction is present. We then consider the case in which the human needs to move the robot directly, by proposing a hybrid admittance/impedance controller, that is again based on a HQP formulation and provides inherent softening when conflicting tasks are present, or in close-to-limit and near-singular configurationsa. This is validated through several experiments in which the human easily moves the robot in the workspace via direct physical interaction. Next, we formulate an additional hierarchy that enables force control and allows to maintain a specific interaction force at the end effector. We then extend this to simultaneous force and trajectory tracking. Overall, we obtain a multi-purpose HQP-based control framework, that seamlessly switchwes between interaction modes, enabling multiple hierarchical behaviours, and covering a wide spectrum of interaction types, essential for synergistic HRI.