Social Humanoid：让人形机器人学会与人协同动作
Social Humanoid: Socially Responsive Human-Humanoid Interaction
arXiv 上这篇把 Unitree G1 训练成能和人互相配合的伙伴，还能预判被挡住的同伴动作，260 小时交互数据开源了。
论文提出 Social Humanoid，一个分层系统，能在 29 自由度的 Unitree G1 上生成并执行响应人类伙伴动作的全身运动。高层策略通过共享 token 化表示对人和机器人的运动做序列建模，能在伙伴被遮挡时预测其动作，低层全身追踪控制器负责在真机上执行。训练上采用两阶段自适应重定向流程，跨 5 个动作捕捉数据集处理交互数据，构建了含 260 小时人-人形交互的 HHI 基准。结果显示系统在仿真和真实世界中都能产出协调、与人对齐的行为。
Social Humanoid: Socially Responsive Human-Humanoid Interaction
Humanoid robots are typically trained as solitary agents, leaving them unable to coordinate physically with human partners who naturally anticipate and adjust to one another. To bridge this gap, we present Social Humanoid, a hierarchical system that generates and executes socially responsive whole-body motion on a 29-DoF Unitree G1 conditioned on a human partner's motion. Its high-level policy performs flexible sequence modeling of human and humanoid motion through a shared tokenized representation, generating reactive humanoid motion and anticipating the partner's motion when they are occluded, while a low-level whole-body tracking controller executes the generated motion on the robot. To train the system, we introduce a two-phase adaptive retargeting procedure that retargets one interaction partner to the robot while morphing the other partner to preserve contact dynamics, reachability, and relative body scale. We apply this procedure across five motion-capture datasets to produce a new Human-Humanoid Interaction (HHI) benchmark of 260 hours of human-humanoid interactions. Our results demonstrate that Social Humanoid produces coordinated, human-aligned behavior across diverse interaction types in simulation and the real world. Project page: https://von31.github.io/social-humanoid/
