6 papers
How Well Do Latent World Models Understand Partially Observable Safety Constraints?
Matthew Kim, Kensuke Nakamura, Andrea Bajcsy
Latent world models are a promising approach for learning state representations and dynamics directly from high-dimensional observations, enabling robot control in hard-to-model se…
How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraints
Kensuke Nakamura, Arun L. Bishop, Steven Man +3
Latent safety filters extend Hamilton-Jacobi (HJ) reachability to operate on latent state representations and dynamics learned directly from high-dimensional observations, enabling…
AnySafe: Adapting Latent Safety Filters at Runtime via Safety Constraint Parameterization in the Latent Space
Sankalp Agrawal, Junwon Seo, Kensuke Nakamura +2
Recent works have shown that foundational safe control methods, such as Hamilton-Jacobi (HJ) reachability analysis, can be applied in the latent space of world models. While this e…
Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures
Junwon Seo, Kensuke Nakamura, Andrea Bajcsy
Recent advances in generative world models have enabled classical safe control methods, such as Hamilton-Jacobi (HJ) reachability, to generalize to complex robotic systems operatin…
Generalizing Safety Beyond Collision-Avoidance via Latent-Space Reachability Analysis
Kensuke Nakamura, Lasse Peters, Andrea Bajcsy
Hamilton-Jacobi (HJ) reachability is a rigorous mathematical framework that enables robots to simultaneously detect unsafe states and generate actions that prevent future failures.…
Not All Errors Are Made Equal: A Regret Metric for Detecting System-level Trajectory Prediction Failures
Kensuke Nakamura, Ran Tian, Andrea Bajcsy
Robot decision-making increasingly relies on data-driven human prediction models when operating around people. While these models are known to mispredict in out-of-distribution int…