SUGAR: 一种可扩展的人类-视频驱动的通用人形机器人运动-操作学习框架
SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework
AI总结：
该研究提出SUGAR框架，通过将多样化的视频转化为可部署的人形机器人运动-操作技能，无需特定任务的奖励工程或参考动作条件，在仿真和现实硬件中实现了六种代表性任务的高性能表现，展示了可扩展性和零样本现实迁移能力。
摘要：
构建能够实现在现实世界中通用的全身体运动-操作能力的人形机器人仍是一个根本性挑战。现有方法要么依赖于繁琐的特定任务奖励工程，要么依赖于僵化的参考动作回放，无法泛化，或者依赖于昂贵的远程操作，限制了可扩展性。尽管人类视频捕捉了多样化的动作行为，但从中推断出的运动先验固有地不完美，受到遮挡、接触伪影和重定向误差的影响，使其不适合直接的策略学习。为此，我们提出了SUGAR，一种可扩展的数据驱动框架，能够将多样化的视频转化为可部署的人形机器人运动-操作技能，无需任何特定任务的奖励工程或参考动作条件。SUGAR分为三个阶段。首先，一个完全自动化的流程从无结构的人类视频中提取运动交互先验，包括人类-物体运动轨迹和接触标签。第二，一个特权物理基础的细化器利用统一的模仿奖励和渐进状态池，将不完美的先验转化为物理上可行的、高保真的技能。第三，经过细化的技能被转化为一个分层的自主策略，包括一个命令生成器和一个命令跟踪器。我们在仿真和现实世界的人形硬件中评估了SUGAR，我们的方法在六种代表性运动-操作任务上显著优于参考跟踪基线，性能随着人类视频数据量的增加而明显提升。它还实现了零样本现实迁移，具有可靠的闭环执行、自主故障恢复和在外部扰动下的稳定长时程性能。项目页面：https://tianshuwu.github.io/sugar-humanoid/
英文摘要：
Building humanoid robots capable of generalizable whole-body loco-manipulation in the real world remains a fundamental challenge. Existing methods either rely on laborious task-specific reward engineering, rigidly replay reference motions that fail to generalize, or depend on costly teleoperation that limits scalability. While human videos capture diverse human behaviors, motion priors inferred from them are inherently imperfect, suffering from occlusion, contact artifacts, and retargeting errors that render them unsuitable for direct policy learning. To address this, we present SUGAR, a scalable data-driven framework that converts diverse human videos into deployable humanoid loco-manipulation skills, without any task-specific reward engineering or reference-motion conditioning at inference. SUGAR proceeds in three stages. First, a fully automated pipeline extracts kinematic interaction priors including human-object motion trajectories and contact labels from unstructured human videos. Second, a privileged physics-based refiner uses a unified mimic reward and progressive state pool to transform imperfect priors into physically feasible, high-fidelity skills. Third, refined skills are distilled into a hierarchical autonomous policy consisting of a command generator and a command tracker. We evaluate SUGAR on six representative loco-manipulation tasks in simulation and real-world humanoid hardware. Our method substantially outperforms reference-tracking baselines, and performance scales clearly with the amount of human video data. It also achieves zero-shot real-world transfer with reliable closed-loop execution, autonomous failure recovery, and stable long-horizon performance under external perturbations. Project Page: https://tianshuwu.github.io/sugar-humanoid/
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