Expanding Safe Sets with Learning-Based Barrier Functions and Reachability

T. Matthews Maheshwari N. Kumar Azra Begzadić S. Tonkens Zhi-Rui Dai Nikolay Atanasov Jorge Cortés Sylvia L. Herbert

· 0 citations · 27 references

TL;DR

GP-CBF is presented, a learning-based framework for synthesizing and continuously adapting a robust control barrier value function for safe robot navigation in unknown environments, ensuring safety and outperforming existing baselines.

Semantic Scholar

View source

Similar papers

Nov 2026

Uncertainty-Calibrated Hamilton-Jacobi Value Learning for Safe Crowd Navigation

Safe navigation in human crowds requires efficiency and reliability. Learning-based policies run fast but can fail under distribution shift, whereas Hamilton–Jacobi (HJ) reachability offers principled safety guarantees but is computationally expensive. We propose a prediction-conditioned reachability framework that bri...

I. Winata, Junghyun Oh · IEEE Robotics and Automation... · 0 citations

Preprint Sep 2026

Online, Reachability-Aware, Sampling-Based Motion Planning

Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. Historically, such approaches have lacked hard safety guarantees, a shortcoming which we remedy in this work by computing guaranteed reachable-set overapproximations onlin...

Brendan T. Gould, Zhi-Yuan Zhang, Panagiotis Tsiotras et al. · 0 citations

Preprint Sep 2026

A Reachability-based Safety Certificate for Dynamical System Motion Policies

Dynamical Systems (DS) are reactive motion policies representing vector fields trained with theoretical guarantees of stability and convergence. To ensure safety during deployment in unknown environments they must be locally reshaped, either through modulation or geometric control barrier function strategies. However,...

Aditya Vats, Tian Xia, Nadia Figueroa · 0 citations

Preprint Oct 2026

Reachability-Guided Sequential Quadratic Programming-Guarded Model Predictive Path Integral for Safe Nonlinear Predictive Control

Safe robot control often requires combining long-horizon performance optimization with hard state and input constraints, but existing approaches tend to address this tradeoff partially. Sampling-based model predictive control (MPC) methods such as model predictive path integral (MPPI) are effective in handling nonlinea...

A. Didier, J. Choi, Namhoon Cho et al. · 0 citations

Preprint Oct 2026

Exact-Safe MPPI: Safety-Aware Sampling with Nonsmooth Control Barrier Functions

Model predictive path integral (MPPI) control plans actions by sampling and evaluating trajectories, but standard MPPI does not guarantee safety. Incorporating control barrier function (CBF) filters into each rollout allows the planner to account for safety corrections. However, combining multiple CBFs through a soft-m...

Alexander Coker, Lei-Lei Cui · 0 citations

Preprint Sep 2026

Graph-Based Safe Reinforcement Learning for Multi-Agent Systems with Time-Varying Topology

A graph-based safe multi-agent reinforcement learning (MARL) framework for cooperative navigation with time-varying topology is presented, integrating a attention-based actor and a Graph Attention Network (GAT) centralized critic, enabling scale-insensitive policy learning under time-varying communication topologies.

Sizhe Xiao, Li-Jing Dong, Rui-Ting Bai et al. · 0 citations