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.

翻译：本文提出一种控制框架，使人形机器人能够与人类伙伴协作执行搬运任务。该框架同时支持平移与旋转运动，这两者是协同搬运场景中的基本运动模式。框架包含三个组成部分：高层规划器、底层控制器及刚度调节机制。在规划层，我们引入交互线性倒立摆模型，结合导纳模型与模型预测控制公式，生成动力学可行的步态规划。这些规划通过基于二次规划的全身控制器执行，该控制器考虑了人形机器人-物体耦合动力学。刚度调节机制用于调控机器人-物体间的相互作用，确保系统收敛至由物体与机器人质心间距离定义的期望相对构型。我们在Digit人形机器人平台上通过实物实验验证了框架的有效性。为量化协作质量，我们提出一种效率度量指标，该指标同时捕获任务性能与智能体间协调性。实验表明该指标能突显柔顺性在协作任务中的作用，并为高层与底层控制层中理想轨迹特性提供见解。最后，我们展示了包括平移、转向及半圆形轨迹等复合运动在内的协作行为实验结果，这些运动代表了自然发生的协同搬运任务。