arXiv AI
ShieldVLA: Feasibility-Aware Safety Alignment for Vision-Language-Action Models
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ActSafeGuard: Differentiable and Training-Aligned Constraint Enforcement for Flow-Matching Policies
arXiv:2609. 11697v1 Announce Type: cross Abstract: 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.
arXiv Machine Learning
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Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
arXiv:2607. 12784v1 Announce Type: cross Abstract: Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation.
Hugging Face Trending Papers
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Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation. However, real-world deployment in open-ended environments requires strong safety guarantees to prevent dangerous or harmful behaviors.
arXiv AI
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CrossSafe: Towards Cross-Embodiment Latent Safety Filters
CrossSafe proposes embodiment-conditioned safety filtering that uses a Hamilton‑Jacobi reachability value function shared across robots while conditioning on each robot’s morphology and kinematics via a morphology‑aware latent representation. The method performs reachability analysis directly in latent space, enabling a single policy trained on multiple bimanual robot embodiments and manipulation tasks to generalize zero‑shot to a held‑out embodiment and reduce collision rates. Experiments on five embodiments and five tasks demonstrate that training with more embodiments improves generalization.
arXiv Machine Learning
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Towards General Language-Conditioned Latent Safety Filters
arXiv:2608. 00315v1 Announce Type: cross Abstract: Robot policies are becoming increasingly general, with vision-language-action (VLA) models enabling a single policy to execute diverse tasks specified in natural language.
arXiv Machine Learning
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APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model
arXiv:2603. 08862v2 Announce Type: replace-cross Abstract: Autonomous navigation in highly constrained environments remains challenging for mobile robots.