NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark

Constant-complexity deterministic memory allocator (heap) for hard real-time high-integrity embedded systems. There is very little activity because the project is finished and does not require further changes.

A collection of work using nonlinear model predictive control (NMPC) with discrete-time control Lyapunov functions (CLFs) and control barrier functions (CBFs)

"Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function" by J. Zeng, B. Zhang and K. Sreenath https://arxiv.org/abs/2007.11718

Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.

Safe robot learning

The Fuzion Language Implementation

ICLR 2024: SafeDreamer: Safe Reinforcement Learning with World Models

"Safety-Critical Control using Optimal-decay Control Barrier Functions with Guaranteed Point-wise Feasibility" by J. Zeng, B. Zhang, Z. Li and K. Sreenath https://arxiv.org/pdf/2103.12375.pdf

Code for the paper "Control Barriers in Bayesian Learning of System Dynamics"

List of NSO Pegasus's C2C Servers where will be used to control Pegasus.

Real Time Safety Heap Allocator

Safety Critical Control of Autonomous Vehicles by Control Barrier Functions

Code for L4DC 2022 paper: Joint Synthesis of Safety Certificate and Safe Control Policy Using Constrained Reinforcement Learning.

This extended Eigen C++ template library and wrapper provide a malloc-free Moore-Penrose pseudoinverse solver.

On the forward invariance of Neural ODEs: performance guarantees for policy learning

🩺🛣️ IBP IoU an approach for the formal verificaion of object detection models.

🚧 🚔 ⚠ Toolbox to compute Criticality Measures for Automated Vehicles

Implementation of the Heatmap-based Unsupervised Debugging of DNNs (HUDD) toolset

Two-Level Segregated Fit - O(1) Memory Allocation Algorithm