Efficient and Compliant Control Framework for Versatile Human-Humanoid Collaborative Transportation

TL;DR

This paper introduces a comprehensive control framework for humanoid robots to perform collaborative transportation tasks with humans, supporting complex motions and ensuring compliance and safety through innovative planning, control, and stiffness modulation, validated on a real humanoid platform.

Contribution

The paper presents a novel integrated control framework combining I-LIP planning, MPC, and stiffness modulation for versatile, compliant human-humanoid collaboration in transportation tasks.

Findings

Framework enables complex translational and rotational co-transport motions.

Experimental validation demonstrates effective collaboration and compliance.

Proposed efficiency metric quantifies collaboration quality and highlights compliance importance.

Abstract

We present a control framework that enables humanoid robots to perform collaborative transportation tasks with a human partner. The framework supports both translational and rotational motions, which are fundamental to co-transport scenarios. It comprises three components: a high-level planner, a low-level controller, and a stiffness modulation mechanism. At the planning level, we introduce the Interaction Linear Inverted Pendulum (I-LIP), which, combined with an admittance model and an MPC formulation, generates dynamically feasible footstep plans. These are executed by a QP-based whole-body controller that accounts for the coupled humanoid-object dynamics. Stiffness modulation regulates robot-object interaction, ensuring convergence to the desired relative configuration defined by the distance between the object and the robot's center of mass. We validate the effectiveness of the framework through real-world experiments conducted on the Digit humanoid platform. To quantify collaboration quality, we propose an efficiency metric that captures both task performance and inter-agent coordination. We show that this metric highlights the role of compliance in collaborative tasks and offers insights into desirable trajectory characteristics across both high- and low-level control layers. Finally, we showcase experimental results on collaborative behaviors, including translation, turning, and combined motions such as semi circular trajectories, representative of naturally occurring co-transportation tasks.