Unitree G1 靠头戴相机躲球真有 95% 成功率，和上帝视角基线差距很小。对做机器人感知和安全的都挺有参考价值。
PAC-MAN 是 arXiv 2607.28623 提出的考虑感知的 CBF-RL 框架，训练时用控制屏障为每个身体链接提供安全约束，部署时只靠头戴相机的分割掩码深度观测球。在单次投掷和往返站位恢复的部署循环基准上，该策略与拥有完整球状态的特权状态 oracle 仅差几分。实验显示 Joint-CBF 在精确球状态时表现最佳，但固定相机观测下仅作为训练引导会退化，加装球跟踪云台或运行时滤波器可恢复。研究者将轻量 Link-CBF 策略零样本部署到 Unitree G1 真机，能容忍不完美感知，95% 的投掷成功，并用语义分割躲避不同球。
PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball
We present PAC-MAN, a perception-aware CBF-RL framework that couples control-barrier safety with deployment-realistic onboard sensing for whole-body humanoid dodgeball. The deployed policy sees the ball only as segmentation-masked depth from a head-mounted camera, while training-time CBF guidance represents clearance to every body link, and an adversarial motion prior regularizes the resulting evasive reflexes. We evaluate on a controlled any-link contact benchmark with seeded throws in two regimes: single throws and a deployment loop in which the robot walks back to its station and recovers between throws. On this benchmark, the policy comes within a few points of a privileged state oracle: a fixed onboard camera alone is adequate for evasion. We find that usable barrier structure depends on perceptual observability: Joint-CBF gives the best performance with accurate ball states, degrades under fixed-camera observations when used only as training guidance, and recovers with a ball-tracking gimbal or privileged runtime filter. We therefore deploy a lightweight Link-CBF policy zero-shot on the Unitree G1 in the real world, where it tolerates imperfect perception, succeeds on 95% of throws, and uses semantic segmentation to dodge different balls.