control-barrier-function-76cb0184·3 events·first seen Aliases: Control-Barrier-Function, Control Barrier Function, Control Barrier Functions
Researchers present PAC-MAN, a framework combining control barrier functions (CBF) with reinforcement learning for whole-body safety in humanoid robots, demonstrated via a dodgeball evasion task. The system uses only onboard depth camera with semantic segmentation, no privileged state information, and is deployed zero-shot on a Unitree G1 humanoid achieving 95% dodge success on real throws. The work advances perception-aware safety for humanoid locomotion by showing that fixed onboard cameras are adequate for reactive whole-body evasion when paired with appropriate barrier structure.
A new arXiv paper proposes a context-conditioned safety critic that learns adaptive clearance preferences for ranking diffusion-based trajectory proposals in indoor robot navigation. The critic decomposes into safety, efficiency, and distance-constraint matching terms, trained with privileged ESDF geometry in simulation and distilled into a perception-only selector via teacher-student learning. The method achieves top success rate and SPL on PointGoal navigation benchmarks (HM3D, MP3D) and transfers zero-shot to a Unitree G1 humanoid robot without task-specific tuning.
A new arXiv preprint introduces Intervention-Aware Variational Quantum Differentiable Predictive Control (IA-VQC-DPC), a framework that trains variational quantum circuit policies under a primal-dual intervention budget to penalize over-reliance on downstream safety filters (Control-Barrier-Function projections). The work also proposes a safety-attribution protocol that decomposes trajectory corrections into policy-level versus filter-level contributions, enabling measurement of whether a policy has genuinely learned safe behavior or is merely being silently repaired by its safety layer. Experiments on BOPTEST building-control emulators show the quantum policy achieves significantly lower pre-filter violations than a matched classical policy at equal parameter budget, with a notable negative result: a learned energy head is only safe when paired with a distribution-aware runtime guard.