List of resources about programming practices for writing safety-critical software.

JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.

µC/OS-II is a preemptive, highly portable, and scalable real-time kernels. Designed for ease of use on a huge number of CPU architectures.

This repository provides a design methodology and approach to building highly-reliable applications on Microsoft Azure for mission-critical workloads.

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

Matlab Interface for Control Barrier Function (CBF) and Control Lyapunov Function (CLF) based control methods.

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.

Replacements to standard numeric types which throw exceptions on errors

🚀 A fast safe reinforcement learning library in PyTorch

A mixed-criticality platform built around Cheshire, with a number of safety/security and predictability features. Ready-to-use FPGA flow on multiple boards is available.

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.

Fast and flexible data logging/tracing toolkit for software testing and debugging. Minimally intrusive C/C++ code instrumentation, host-based decoding application, demo code included.

Official Code for Paper: Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications

The Verifiably Safe Reinforcement Learning Framework

Bourne shell, template engine, scripting language reliable, scalable projects. Based a ISO standard proven effective for large, mission-critical projects, SparForte is designed for fast development while, at the same time, providing easier designing, maintenance and bug removal. About 130.000 lines of code.

🚗 A repository for documenting and exploring the world of autonomous driving safety, featuring a curated collection of research papers, reports, and resource.

Repository containing the code for the paper "Safe Model-Based Reinforcement Learning using Robust Control Barrier Functions". Specifically, an implementation of SAC + Robust Control Barrier Functions (RCBFs) for safe reinforcement learning in two custom environments

A list of papers that studies out-of-distribution (OOD) detection and misclassification detection (MisD)

Safety-critical controllers for single/multi robotic navigation: CBF-QP, MPC-CBF, and etc.

Bare Metal Board Support Package for Texas Instruments Cortex-R4F/R5F TMS570