KinoGen：基于运动学约束视频生成的可定制人形机器人移动操作参考生成框架
KinoGen: Customizable Humanoid Loco-Manipulation References via Kinematically Grounded Video Generation
AI总结：
KinoGen是基于运动学约束视频生成的框架，可合成人形机器人移动操作参考，在8项任务中性能优于基线，其定制参考满足指定目标，相关跟踪策略已部署在Unitree G1人形机器人上。
摘要：
我们提出KinoGen，这是一个通过运动学约束视频生成合成可定制人形机器人移动操作参考的框架。KinoGen将预训练视频生成器以模拟任务实例中目标机器人与物体的校准渲染结果为条件，通过将相同模型拟合到生成的双视图视频来恢复参考，从而使该参考天然处于机器人构型空间中，无需人体代理或重定向。身体、手部及物体先验初始化重建过程，针对跨视图证据的直接优化对其进行细化。由于编辑后的关键帧仅为另一种渲染状态，用户可指定目标物体位姿，围绕编辑后的关键帧重新生成视频，得到完全满足该位姿的参考。在8项移动操作任务中，KinoGen相比基于视频的基线方法产生更低的穿透量与脚部滑动，保留超过90%的标注接触关系，其定制参考满足所有指定目标，且穿透量远低于直接插值方法。基于合成参考训练的跟踪策略已部署在配备灵巧手部的Unitree G1人形机器人上。项目网页：this https URL。
英文摘要：
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/.
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。
