发表机构
University of California San Diego; Yonsei University（加州大学圣迭戈分校; 延世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出OCLO系统，无需人类数据，仅通过末端执行器目标实现人形机器人移动操作，利用解析可达性先验和策略在环细化生成姿态，并学习全身顺从性，显著提高成功率并保持平衡。
AI 中文摘要
大多数人体移动操作控制器需要人类运动数据来学习全身协调和姿态，使得策略依赖外部来源提供这些数据。我们提出了OCLO（在线姿态顺从性移动操作），一个无需人类运动数据训练且仅通过两个末端执行器目标进行指令控制的人形机器人移动操作系统。由于这些目标不能唯一确定全身姿态，OCLO使用解析可达性先验在线生成骨盆高度和躯干方向，并通过策略在环采样与任务无关成本进一步细化。OCLO还通过弹簧-阻尼模型根据测量力位移末端执行器参考来学习全身顺从性，鼓励腿部、腰部和骨盆屈服于外部负载。在仿真中，使用可达性先验使得获取指令参考的成功率达到77.8%，远高于不使用先验时的37.8%。此外，细化降低了所有评估任务中的末端执行器方向误差。相同的姿态模块在五个任务中的四个上改进了预训练的SONIC控制器。没有顺从性训练，策略在干扰下倾向于失去平衡而不是牺牲跟踪。在Unitree G1上，OCLO在导致其消融失败的末端执行器干扰下保持平衡，并执行七项移动操作任务，包括蹲行和从低表面捡起箱子。项目网站：此https URL
英文摘要
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/
