List of resources about programming practices for writing safety-critical software.
-
Updated
Apr 23, 2024 - Python
List of resources about programming practices for writing safety-critical software.
JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.
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
Safety-critical controllers for single/multi robotic navigation: CBF-QP, MPC-CBF, and etc.
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)
Bare Metal Board Support Package for Texas Instruments Cortex-R4F/R5F TMS570
Add a description, image, and links to the safety-critical topic page so that developers can more easily learn about it.
To associate your repository with the safety-critical topic, visit your repo's landing page and select "manage topics."