Human-humanoid collaboration shows significant promise for applications in healthcare, domestic assistance, and manufacturing. While compliant robot-human collaboration has been extensively developed for robotic arms, enabling compliant human-humanoid collaboration remains largely unexplored due to humanoids' complex whole-body dynamics. In this paper, we propose a proprioception-only reinforcement learning approach, COLA, that combines leader and follower behaviors within a single policy. The model is trained in a closed-loop environment with dynamic object interactions to predict object motion patterns and human intentions implicitly, enabling compliant collaboration to maintain load balance through coordinated trajectory planning. We evaluate our approach through comprehensive simulator and real-world experiments on collaborative carrying tasks, demonstrating the effectiveness, generalization, and robustness of our model across various terrains and objects. Simulation experiments demonstrate that our model reduces human effort by 24.7%. compared to baseline approaches while maintaining object stability. Real-world experiments validate robust collaborative carrying across different object types (boxes, desks, stretchers, etc.) and movement patterns (straight-line, turning, slope climbing). Human user studies with 23 participants confirm an average improvement of 27.4% compared to baseline models. Our method enables compliant human-humanoid collaborative carrying without requiring external sensors or complex interaction models, offering a practical solution for real-world deployment.

翻译：人形机器人与人的协作在医疗保健、家庭辅助和制造领域展现出巨大应用潜力。尽管针对机械臂的柔顺人机协作已得到广泛发展，但由于人形机器人复杂的全身动力学特性，实现柔顺的人形机器人与人协作在很大程度上仍未被探索。本文提出了一种仅依赖本体感觉的强化学习方法COLA，该方法将领导者与跟随者行为整合在单一策略中。模型在具有动态物体交互的闭环环境中进行训练，以隐式预测物体运动模式与人类意图，通过协调轨迹规划实现柔顺协作以维持负载平衡。我们通过协同搬运任务的全面仿真与真实世界实验评估所提方法，证明了模型在不同地形与物体条件下的有效性、泛化性与鲁棒性。仿真实验表明，相较于基线方法，我们的模型在保持物体稳定性的同时将人力消耗降低了24.7%。真实世界实验验证了模型在不同物体类型（箱子、桌子、担架等）与运动模式（直线行进、转弯、爬坡）下的鲁棒协同搬运能力。包含23名参与者的人类用户研究证实，相较于基线模型平均有27.4%的性能提升。我们的方法无需外部传感器或复杂交互模型即可实现柔顺的人形机器人与人协同搬运，为实际场景部署提供了实用解决方案。