CoorDex: 协调身体与手部先验以实现连续灵巧类人运动操作
CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation
AI总结：
提出CoorDex学习框架，通过将高维身体和灵巧手控制转化为协调的潜在残差控制，实现类人机器人在运动中执行灵巧操作，如不停顿抓取瓶子、移动中开冰箱门等。
摘要：
类人运动操作常被简化为走走停停的过程：走到物体旁，停下操作，然后恢复运动。此外，它通常依赖于低自由度（DoF）的末端执行器，表现为开合抓取基元。我们提出CoorDex，一种学习流水线，将高维身体和灵巧手控制转化为协调的潜在残差控制，实现高自由度灵巧运动操作。从模拟的全身和手部演示开始，CoorDex训练类人身体和灵巧手的特权运动跟踪教师，将其蒸馏为基于本体感觉的潜在先验，并将冻结的先验作为下游残差强化学习的动作空间。协调的潜在残差策略通过共享任务上下文和分离的身体-手部残差头组合这些先验，在保持自然全身运动的同时提高手指级接触可靠性。CoorDex使配备20自由度WUJI手的Unitree G1类人机器人能够在运动中执行灵巧操作，包括不停顿抓取和搬运瓶子、移动中打开冰箱门以及立方体抓取和翻转。在行走-抓取-搬运任务上的消融实验表明，在相同奖励预算下，关节空间PPO、关节空间手部控制和单一潜在预测均失败，而潜在先验接口和协调残差结构使高维接触丰富的运动操作变得可训练。项目页面：this https URL
英文摘要：
Humanoid loco-manipulation is often simplified into a stop-and-go process: walking to an object, stopping to manipulate it, and then resuming locomotion. It also commonly relies on low degree-of-freedom (DoF) end effectors that behave like an open-close grasp primitive. We introduce CoorDex, a learning pipeline that converts high-dimensional body and dexterous hand control into coordinated latent residual control, enabling high-DoF dexterous loco-manipulation on the move. Starting from simulated whole-body and hand demonstrations, CoorDex trains privileged motion tracking teachers for the humanoid body and dexterous hand, distills them into proprioception-conditioned latent priors, and uses the frozen priors as the action space for downstream residual reinforcement learning. A coordinated latent residual policy composes these priors through shared task context and separate body-hand residual heads, preserving natural whole-body motion while improving finger-level contact reliability. CoorDex enables a Unitree G1 humanoid with a 20-DoF WUJI hand to execute dexterous manipulation while in motion, including non-stop bottle grasping and carrying, fridge door opening on the move, and cube pick-and-turn. Ablations on the walk-grasp-carry task show that joint-space PPO, joint-space hand control, and monolithic latent prediction all fail under the same reward budget, while the latent-prior interface and coordinated residual structure make high-dimensional contact-rich loco-manipulation trainable. Project Page: https://skevinci.github.io/coordex/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
