AI 中文总结
HIGenNTO通过优化预训练运动模型的噪声，在稀疏约束下生成可执行的人形交互运动，并支持编码智能体编写任务，实现从高层描述到物理交互的可扩展路径。
AI 中文摘要
人形机器人可以通过模仿运动学上的人形运动参考来获取复杂技能，然而，对于接触丰富的交互，可靠的参考仍然难以获得：动作捕捉在遮挡和紧密物理接触下会退化，而重定向则引入了额外的接触和几何不一致性。我们提出了HIGenNTO，一个通过优化预训练文本条件运动模型的初始噪声，在稀疏时空和场景约束下合成人形-场景交互运动参考的框架。该公式在保持先验的真实感和时间连贯性的同时，满足期望的接触、避免碰撞并维持稳定支撑，从而从零开始生成交互运动，并分阶段组合长时域行为。在机器人-环境和机器人-物体任务中，HIGenNTO生成的运动可以通过跟踪策略在仿真中执行，并用于训练仅依赖机载传感的深度条件视觉运动策略。我们在Unitree G1上部署这些策略，跨越四个接触丰富的任务。最后，任务规范本身可以由编码智能体编写，该智能体提出交互任务并将其编译为提示、约束和场景程序，编写了我们八个评估任务中的三个以及另外四个行为。总之，这些结果建立了一条从高层任务描述到物理可执行的人形交互的可扩展路径。
英文摘要
Humanoid robots can acquire complex skills by imitating kinematic humanoid motion references, yet reliable references for contact-rich interactions remain difficult to obtain: motion capture deteriorates under occlusion and close physical contact, while retargeting introduces additional contact and geometric inconsistencies. We present HIGenNTO, a framework that synthesizes humanoid-scene interaction motion references by optimizing the initial noise of a pretrained text-conditioned motion model under sparse spatiotemporal and scene constraints. The same formulation satisfies desired contacts, avoids collisions, and maintains stable support while retaining the prior's realism and temporal coherence, generating interaction motions from scratch and composing long-horizon behaviors stage-wise. Across robot-environment and robot-object tasks, HIGenNTO produces motions that can be executed by tracking policies in simulation and used to train depth-conditioned visuomotor policies operating solely from onboard sensing. We deploy these policies on a Unitree G1 across four contact-rich tasks. Finally, the task specifications themselves can be written by a coding agent, which proposes interaction tasks and compiles them into prompt, constraint, and scene programs, authoring three of our eight evaluated tasks and four further behaviors. Together, these results establish a scalable path from high-level task descriptions to physically executable humanoid interactions.
