NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
A framework for the elicitation, specification, formalization and analysis of requirements.
Constant-complexity, deterministic, very fast memory allocator (heap) for hard real-time high-integrity embedded systems. Allocation takes ≈120 cycles @ RP2350 irrespective of heap usage. There is little activity because the project is finished and does not require further changes.
"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
A collection of work using nonlinear model predictive control (NMPC) with discrete-time control Lyapunov functions (CLFs) and control barrier functions (CBFs)
Safe robot learning
ICLR 2024: SafeDreamer: Safe Reinforcement Learning with World Models
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.
The Fuzion Language Implementation
"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"
Real Time Safety Heap Allocator
🚧 🚔 ⚠ Toolbox to compute Criticality Measures for Automated Vehicles
Safety Critical Control of Autonomous Vehicles by Control Barrier Functions
This extended Eigen C++ template library and wrapper provide a malloc-free Moore-Penrose pseudoinverse solver.
Code for L4DC 2022 paper: Joint Synthesis of Safety Certificate and Safe Control Policy Using Constrained Reinforcement Learning.
On the forward invariance of Neural ODEs: performance guarantees for policy learning
🩺🛣️ IBP IoU an approach for the formal verificaion of object detection models.
A metamodel to support Executable UML based on Shlaer-Mellor semantics
[ICML 2025] A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
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