ActSafeGuard: Differentiable and Training-Aligned Constraint Enforcement for Flow-Matching Policies
Vision-Language-Action (VLA) and World-Action Models (WAMs) have demonstrated strong capabilities in general-purpose robotic manipulation, yet their generated actions may violate hard physical constraints and therefore be unsafe or infeasible for deployment. Existing safety approaches either optimize statistical safety objectives without deterministic per-step guarantees or correct unsafe actions only during inference, creating a mismatch between policy training and execution. We introduce ActSafeGuard, a differentiable and training-aligned safeguard layer for flow-matching based policies. ActSafeGuard integrates hard action feasibility into policy learning, not merely treating safety as ...
| Authors | Jianming Ma, Rongjun Jin, Xiaxi Si et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 September 2026 |
| Last updated at source | 14 September 2026 |
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