标题： ULC：一种用于人形机器人运动操作的统一且细粒度控制器 显示英文标题

标题： ULC: A Unified and Fine-Grained Controller for Humanoid Loco-Manipulation

摘要： 人形机器人Loco-Manipulation旨在使机器人能够将移动性与上半身跟踪能力相结合。 现有的大多数方法采用分层架构，将控制分解为独立的上半身（操作）和下半身（移动）策略。 虽然这种分解降低了训练复杂性，但其本质上限制了子系统之间的协调，并与人类表现出的统一全身控制相矛盾。 我们证明，一个统一的策略可以实现人形Loco-Manipulation的跟踪精度、大工作空间和鲁棒性。 我们提出了统一的Loco-Manipulation控制器（ULC），这是一个单一策略框架，以端到端的方式同时跟踪根速度、根高度、躯干旋转和双臂关节位置，证明了统一控制的可行性而不会牺牲性能。 我们通过关键技术实现了这种统一控制：序列技能获取用于逐步学习复杂性，残差动作建模用于精细控制调整，命令多项式插值用于平滑运动过渡，随机延迟释放用于应对部署变化的鲁棒性，负载随机化用于对外部干扰的泛化，以及重心跟踪用于提供显式的策略梯度以保持稳定性。 我们在具有3-DOF（自由度）腰部的Unitree G1人形机器人上验证了我们的方法。 与强基线相比，ULC在解耦方法中表现出更好的跟踪性能，并展示了更大的工作空间覆盖范围。 统一的双臂跟踪能够在外部负载下进行精确操作，同时保持协调的全身控制以完成复杂的Loco-Manipulation任务。 显示英文摘要

摘要： Loco-Manipulation for humanoid robots aims to enable robots to integrate mobility with upper-body tracking capabilities. Most existing approaches adopt hierarchical architectures that decompose control into isolated upper-body (manipulation) and lower-body (locomotion) policies. While this decomposition reduces training complexity, it inherently limits coordination between subsystems and contradicts the unified whole-body control exhibited by humans. We demonstrate that a single unified policy can achieve a combination of tracking accuracy, large workspace, and robustness for humanoid loco-manipulation. We propose the Unified Loco-Manipulation Controller (ULC), a single-policy framework that simultaneously tracks root velocity, root height, torso rotation, and dual-arm joint positions in an end-to-end manner, proving the feasibility of unified control without sacrificing performance. We achieve this unified control through key technologies: sequence skill acquisition for progressive learning complexity, residual action modeling for fine-grained control adjustments, command polynomial interpolation for smooth motion transitions, random delay release for robustness to deploy variations, load randomization for generalization to external disturbances, and center-of-gravity tracking for providing explicit policy gradients to maintain stability. We validate our method on the Unitree G1 humanoid robot with 3-DOF (degrees-of-freedom) waist. Compared with strong baselines, ULC shows better tracking performance to disentangled methods and demonstrating larger workspace coverage. The unified dual-arm tracking enables precise manipulation under external loads while maintaining coordinated whole-body control for complex loco-manipulation tasks.