6 papers · 1 filter
V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions
Mumuksh Tayal, Manan Tayal, Aditya Singh +2
Ensuring safety in autonomous systems requires controllers that aim to satisfy state-wise constraints without relying on online interaction.While existing Safe Offline RL methods t…
MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control
Manan Tayal, Aditya Singh, Shishir Kolathaya +1
Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-agent reinforcement learning (MARL), saf…
Safe and Performant Controller Synthesis using Gradient-based Model Predictive Control and Control Barrier Functions
Aditya Singh, Aastha Mishra, Manan Tayal +2
Ensuring both performance and safety is critical for autonomous systems operating in real-world environments. While safety filters such as Control Barrier Functions (CBFs) enforce…
Neural Control Barrier Functions from Physics Informed Neural Networks
Shreenabh Agrawal, Manan Tayal, Aditya Singh +1
As autonomous systems become increasingly prevalent in daily life, ensuring their safety is paramount. Control Barrier Functions (CBFs) have emerged as an effective tool for guaran…
CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions
Manan Tayal, Aditya Singh, Pushpak Jagtap +1
Control Barrier Functions (CBFs) are a practical approach for designing safety-critical controllers, but constructing them for arbitrary nonlinear dynamical systems remains a chall…
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
Manan Tayal, Aditya Singh, Shishir Kolathaya +1
As autonomous systems become more ubiquitous in daily life, ensuring high performance with guaranteed safety is crucial. However, safety and performance could be competing objectiv…