Humanoid loco-manipulation holds transformative potential for daily service and industrial tasks, yet achieving precise, robust whole-body control with 3D end-effector force interaction remains a major challenge. Prior approaches are often limited to lightweight tasks or quadrupedal/wheeled platforms. To overcome these limitations, we propose FALCON, a dual-agent reinforcement-learning-based framework for robust force-adaptive humanoid loco-manipulation. FALCON decomposes whole-body control into two specialized agents: (1) a lower-body agent ensuring stable locomotion under external force disturbances, and (2) an upper-body agent precisely tracking end-effector positions with implicit adaptive force compensation. These two agents are jointly trained in simulation with a force curriculum that progressively escalates the magnitude of external force exerted on the end effector while respecting torque limits. Experiments demonstrate that, compared to the baselines, FALCON achieves 2x more accurate upper-body joint tracking, while maintaining robust locomotion under force disturbances and achieving faster training convergence. Moreover, FALCON enables policy training without embodiment-specific reward or curriculum tuning. Using the same training setup, we obtain policies that are deployed across multiple humanoids, enabling forceful loco-manipulation tasks such as transporting payloads (0-20N force), cart-pulling (0-100N), and door-opening (0-40N) in the real world.

翻译：人形机器人的移动操作在日常服务和工业任务中具有变革性潜力，然而，在三维末端执行器力交互下实现精确、鲁棒的整体控制仍然是一个重大挑战。现有方法通常局限于轻量级任务或四足/轮式平台。为克服这些限制，我们提出了FALCON，一种基于双智能体强化学习的鲁棒力自适应人形机器人移动操作框架。FALCON将整体控制分解为两个专门化的智能体：(1) 一个下半身智能体，确保在外部力扰动下的稳定移动；(2) 一个上半身智能体，通过隐式自适应力补偿精确跟踪末端执行器位置。这两个智能体在仿真中通过力课程联合训练，该课程在尊重扭矩限制的同时，逐步增加施加于末端执行器的外部力大小。实验表明，与基线方法相比，FALCON实现了2倍更精确的上半身关节跟踪，同时在力扰动下保持鲁棒的移动性，并获得了更快的训练收敛速度。此外，FALCON支持无需针对具体机器人形态进行奖励或课程调整的策略训练。使用相同的训练设置，我们获得了可部署于多个人形机器人的策略，实现了现实世界中强力的移动操作任务，例如运输负载（0-20N力）、拉车（0-100N）和开门（0-40N）。