Humanoid robots have achieved impressive locomotion performance, yet contact-rich and long-horizon manipulation remains a major bottleneck. Manipulation is inherently contact-rich and demands compliant whole-body control for stable interaction, while its diversity and long-horizon nature favor modular, planner-compatible interfaces over joint-space tracking. We propose CEER, a compliant end-effector-root (EE-root) control abstraction for modular humanoid loco-manipulation within a hierarchical planning framework. CEER enables compliance-aware whole-body control in an interpretable task space defined by root motion commands and end-effector pose targets, and supports plug-and-play integration with heterogeneous high-level planners. A teacher-student framework is adopted to distill a general motion-tracking controller into a low-level policy that consumes only EE-root commands. We further construct a hierarchical system that integrates heterogeneous planners and task modules through the EE-root interface, enabling diverse manipulation tasks without retraining the underlying whole-body policy. Experiments in simulation and on hardware demonstrate 3.3 cm end-effector tracking accuracy with substantially reduced jerk compared to baselines, stable contact-rich manipulation under teleoperation, and up to 70% success in simulated single-object loco-manipulation tasks within a room-scale environment. These results indicate that compliant EE-root control provides a practical abstraction for humanoid loco-manipulation, enabling modular and scalable integration of diverse skills.

翻译：摘要：人形机器人在运动性能方面已取得显著进展，但涉及密集接触与长时域的操作仍是主要瓶颈。操作任务天然具有接触密集特性，需要顺从性全身控制以实现稳定交互，同时其多样性与长时域特性更适用模块化、与规划器兼容的接口而非关节空间跟踪。我们提出CEER——一种面向分层规划框架中模块化人形移动操作的顺从性末端执行器-根部（EE-root）控制抽象方法。CEER可在由根部运动指令和末端执行器位姿目标定义的可解释任务空间中实现具备顺从感知的全身控制，并支持与异构高层规划器的即插即用集成。采用教师-学生框架将通用运动跟踪控制器蒸馏为仅消耗EE-root命令的低层级策略。我们进一步构建了通过EE-root接口集成异构规划器与任务模块的分层系统，无需重新训练底层全身策略即可实现多样化操作任务。仿真与硬件实验表明，与基线方法相比，末端执行器跟踪精度达3.3厘米且冲击度显著降低，遥操作下可实现稳定的密集接触操作，在房间尺度环境中的模拟单物体移动操作任务成功率高达70%。这些结果表明，顺从性EE-root控制可作为人形移动操作的有效抽象方法，实现多样化技能的模块化与可扩展集成。