外观
论文arXiv 2610.2055214 天前
KinoGen：通过运动学扎根的视频生成实现可定制的人形机器人移动操作参考
KinoGen通过运动学约束视频生成合成人形机器人移动操作参考，并在八项任务和Unitree G1上验证。
宇树 G1原文链接：KinoGen: Customizable Humanoid Loco-Manipulation References via Kinematically Grounded Video Generation（arXiv 2610.20552 · 发布日期：2026-10-14）
KinoGen: Customizable Humanoid Loco-Manipulation References via Kinematically Grounded Video Generation
KinoGen通过运动学约束视频生成合成人形机器人移动操作参考，并在八项任务和Unitree G1上验证。
核心要点
- 面向人形机器人移动操作参考生成，避免依赖人体代理或重定向带来的配置空间不一致。
- 方法以目标机器人和物体的校准渲染调节预训练视频生成器，再从双视角视频拟合恢复参考。
- 身体、手和物体先验用于初始化，跨视角证据直接优化细化重建结果。
- 在八项任务中较视频基线降低穿透和脚滑，保留超90%标注接触；定制目标均满足。
验证范围与局限：验证范围包括八项移动操作任务，跟踪策略部署在带灵巧手的Unitree G1人形机器人上。
英文摘要 (Abstract)
We present KinoGen, a framework that synthesizes customizable humanoid loco-manipulation references through kinematically grounded video generation. KinoGen conditions a pretrained video generator on calibrated renders of the target robot and object in a simulated task instance and recovers the reference by fitting the same models to the generated two-view video, so that the reference lies in the robot configuration space by construction without a human body proxy or retargeting. Body, hand, and object priors initialize the reconstruction, and direct optimization against cross-view evidence refines it. Since an edited keyframe is simply another rendered state, a user can prescribe a goal object pose, regenerate the video around the edited keyframe, and obtain a reference that satisfies the pose exactly. Across eight loco-manipulation tasks, KinoGen yields lower penetration and foot skating than video-based baselines and preserves over 90% of annotated contacts, and its customized references satisfy every prescribed target with substantially less penetration than direct interpolation. Tracking policies trained on the synthesized references are deployed on a Unitree G1 humanoid with dexterous hands. Project webpage: https://kinogen-humanoid.github.io/.
作者：Qi Wu, Lulu ZHAO, Sanghyun Hahn, Rishi Yennu, Emmy Yang, Dingqi Zhang, Tao Pang, Jiuguang Wang, Kuan Fang
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