PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball
Score
76首次系统研究CBF-RL在真实机载感知下的人形机器人全身躲球，揭示了感知可观测性与CBF结构性能的关系。
方法设计严谨，包含仿真基准、消融研究和真实世界部署验证，实验较完整。
提出的零样本部署策略在Unitree G1上达到95%成功率，对机器人安全控制落地有直接参考价值。
结合CBF与RL的安全控制是当前机器人学热门方向，该论文紧跟感知现实性需求。
来源为arXiv AI/CL/LG预印本，作者机构背景扎实，且包含真实机器人实验。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The paper systematically combines CBF with RL, addresses perceptual realism, and provides thorough experiments with real-world deployment.
AI commentary
「What excites me most is that this paper liberates the safety layer from the assumption of omniscient perception and confronts real sensor noise. For engineers deploying robots, a 95% real-world dodgeball success rate is a hard metric, proving the method isn't just simulation-pretty. If you work on safety-critical RL or humanoid behavior control, this is worth a deep read.」
Quick overview
This paper presents PAC-MAN, a perception-aware CBF-RL framework that couples control-barrier safety with deployment-realistic onboard sensing for whole-body humanoid dodgeball. The policy uses only segmentation-masked depth from a head-mounted camera, with training-time CBF guidance for clearance to every body link and an adversarial motion prior to regularize evasive reflexes. Evaluated on a controlled any-link contact benchmark with seeded throws, the policy approaches a privileged state oracle in both single-throw and deployment-loop regimes. The authors find that barrier structure depends on perceptual observability: Joint-CBF performs best 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. They deploy a lightweight Link-CBF policy zero-shot on the Unitree G1 in the real world, achieving 95% throw evasion success and semantic segmentation to dodge different balls.
- Proposes PAC-MAN, coupling CBF safety constraints with realistic perception for whole-body humanoid dodgeball.
- Uses only segmentation-masked depth from a head-mounted camera, approaching privileged oracle performance.
- Barrier structure depends on perceptual observability; Joint-CBF degrades under fixed-camera observations but recovers with a ball-tracking gimbal.
- Zero-shot deployment on Unitree G1 achieves 95% real-world throw evasion, with semantic segmentation for different balls.
Full text
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