外观
论文arXiv 2610.0567823 天前
免数据集的顺应性人形机器人移动操作与动态在线姿态
OCLO无需人类运动数据，用在线姿态生成与顺应性学习实现人形移动操作，并在仿真和Unitree G1上验证。
宇树 G1原文链接：Dataset-Free Compliant Humanoid Loco-Manipulation with Dynamic Online Posture（arXiv 2610.05678 · 发布日期：2026-10-05）
Dataset-Free Compliant Humanoid Loco-Manipulation with Dynamic Online Posture
OCLO无需人类运动数据，用在线姿态生成与顺应性学习实现人形移动操作，并在仿真和Unitree G1上验证。
核心要点
- 多数人形移动操作控制器依赖人类运动数据学习全身协调与姿态。
- OCLO仅由两个末端目标指令，结合可达性先验和策略在环采样在线生成姿态。
- 通过弹簧-阻尼模型按测得力偏移末端参考，学习全身对外载荷的顺应。
- 仿真中可达性先验将参考获取成功率由37.8%提升至77.8%，真机完成七项任务。
验证范围与局限：验证包括仿真任务和一台Unitree G1真机上的七项移动操作任务。
英文摘要 (Abstract)
Most humanoid loco-manipulation controllers require human motion data to learn whole-body coordination and posture, leaving policies reliant on external sources to provide this data. We present OCLO (Online-posture Compliant LOco-manipulation), a humanoid loco-manipulation system trained without human motion data and commanded only through two end-effector targets. Because these targets do not uniquely determine whole-body posture, OCLO generates pelvis height and torso orientation online using an analytic reachability prior, further refined through policy-in-the-loop sampling with a task-agnostic cost. OCLO also learns whole-body compliance by displacing end-effector references according to measured forces through a spring-damper model, encouraging the legs, waist, and pelvis to yield to external loads. In simulation, using the reachability prior leads to a 77.8% success rate in acquiring the commanded reference, a vast improvement over the 37.8% success rate accomplished without the prior. Further, refinement reduces end-effector orientation error across all evaluated tasks. The same posture module improves a pretrained SONIC controller on four of five tasks. Without compliance training, policies tend to lose balance under disturbances rather than sacrifice tracking. On a Unitree G1, OCLO maintains balance under end-effector disturbances that cause its ablations to fail and performs seven loco-manipulation tasks, including crouched walking and picking up a box from a low surface. Project website: https://oclo-humanoid.github.io/
作者：Seungho Yeom, Zhenyu Wu, Jaeyoung Huh, Diego Williams, Yuheng Zhi, Soofiyan Atar, Michael C. Yip
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