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.
By Jianming Ma, Rongjun Jin, Xiaxi Si, Yang Zhang, Yiheng Li, Yue Gao
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.
By Ihab Tabbara, Yuxuan Yang, Hussein Sibai
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.
By Paolo Magliano, Puze Liu, Jan Peters, Davide Tateo, Raffaello Camoriano
arXiv:2607. 14424v1 Announce Type: cross Abstract: In recent years Flow Matching has become a prominent method for generative modeling robot motion generation.
By Nutan Chen, Jianxiang Feng, Marvin Alles, Botond Cseke
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.
AegisFlow is a training‑free framework that guides generative models, such as diffusion and flow matching, along safe paths by formulating the problem as a global optimal control task. It incorporates a time‑varying barrier function into the cost functional, enabling non‑myopic enforcement of constraints throughout the entire generative trajectory. Experiments show that AegisFlow outperforms existing methods in text‑guided image manipulation and robotic planning, achieving a better balance between target alignment and constraint satisfaction.
By Kunpeng Liu, Boshi Zhang, Keyou You