arXiv:2610.16522v2 Announce Type: replace-cross Abstract: Safe robot navigation in unknown, dynamic environments requires more than geometric collision checking: a robot still far from an obstacle ma...
arXiv:2610.27870v1 Announce Type: cross Abstract: Hamilton-Jacobi (HJ) reachability analysis offers formal safety guarantees for control systems, but training accurate neural certificates remains dif...
The paper introduces LEAP-CBF, a safety filter that uses Least‑Effort Adversarial Potentials to quantify how much disturbance effort is needed to cause failure in nonlinear dynamical systems. LEAP serves as a control barrier function for the undisturbed system and can be combined with a robust safety filter that tolerates disturbances with bounded cumulative effort. The authors develop a deep reinforcement learning method to construct LEAPs and validate their effectiveness through simulations of multi‑agent systems and hardware experiments on a quadruped and quadrotors.
arXiv:2609.06433v1 Announce Type: cross Abstract: The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb...
arXiv:2608.06481v2 Announce Type: replace-cross Abstract: Synthesizing safe and robust neural controllers in simulation for reliable sim-to-real deployment remains a critical challenge in robotics. E...
FAITH is a model‑free reinforcement learning framework that separates safety from task performance by using a learned safety filter. The filter approximates the optimal state‑action safety value with a feedforward network, allowing the task policy to optimize return through filtered dynamics without a competing safety term. Experiments on a double integrator, Safety Gym, a 29‑DoF humanoid, and a real Unitree G1 show that FAITH achieves high returns while maintaining near‑perfect safety and handling infeasible starts gracefully.
arXiv:2606. 30935v1 Announce Type: cross Abstract: While neural network control policies are powerful, their deployment on safety critical systems depends on ensuring that they obey strict constraints.
arXiv:2602.08054v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) offers a promising framework for deploying autonomous systems in safety-critical settings without the ris...
arXiv:2609.22762v1 Announce Type: new Abstract: Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expe...
The paper proves that using a permissive safety filter in reinforcement learning does not compromise asymptotic performance. By formalizing safety through a safety‑critical Markov decision process and a filtered MDP, the authors show that optimal policies in the filtered MDP achieve the same return as the best safe policy in the original setting. Experiments on Safety Gymnasium confirm zero violations during training and performance that matches or exceeds unfiltered baselines.
arXiv:2605. 28983v2 Announce Type: replace-cross Abstract: In this paper, training a neural network is identified, exactly, as a search through Hamilton--Jacobi initial-value problems: each gradient step selects the initial data of a viscous Hamilton--Jacobi equation whose Hopf--Cole propagator best fits the observations; at inference, the input is the spatial point at which that solution is evaluated and the initial condition is already encoded in the weights.
arXiv:2606. 16515v1 Announce Type: cross Abstract: Hamilton-Jacobi-Bellman theory implies that the optimal goal-conditioned action depends on the goal only through the gradient of the goal-reaching distance at the current state, yet standard online GCRL still conditions the actor on the raw goal -- a signal that is geometrically uninformative when the goal is far from the data distribution.