neu-autonomy / nfl_veripy
Formal Verification of Neural Feedback Loops (NFLs)
☆77Updated last month
Related projects ⓘ
Alternatives and complementary repositories for nfl_veripy
- Learning Certified Control Using Contraction Metric (CoRL 2020)☆30Updated last year
- ☆62Updated 8 months ago
- Code needed to reproduce the examples found in "Learning Control Barrier Functions from Expert Demonstrations," by A. Robey, H. Hu, L. Li…☆56Updated last year
- Learning Lyapunov functions and control policies of nonlinear dynamical systems☆119Updated 3 years ago
- Path planning from STL (Signal Temporal Logic) specifications☆49Updated last year
- Toolkit for learning controllers based on robust control Lyapunov barrier functions☆137Updated 3 months ago
- Optimizing Dynamic Programming-Based Algorithms☆102Updated this week
- Safe exploration in Markov Decision Processes☆38Updated 6 years ago
- PyTorch Official Implementation of CoRL 2023 Paper: Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-a…☆23Updated 5 months ago
- ☆81Updated last year
- Lyapunov-stable Neural Control for State and Output Feedback☆57Updated 3 months ago
- A python library for control from Signal Temporal Logic (STL) specifications☆35Updated 2 years ago
- Robust Online Motion Planning using Contraction Theory☆53Updated 5 years ago
- Synthesizing neural-network Lyapunov functions (and controllers) as stability certificate.☆139Updated 10 months ago
- We use reachability to ensure the safety of a decision agent acting on a dynamic system in real-time. We compute the Forward Reachable Se…☆31Updated 3 years ago
- Adaptive control-oriented meta-learning for nonlinear systems☆54Updated 3 years ago
- The aim of this repo is to bring ideas and relevant literature relating to Safe-RL in the context of autonomous vehicles.☆47Updated 6 years ago
- Code for the paper "Control Barriers in Bayesian Learning of System Dynamics"☆23Updated 2 years ago
- Codes for designing Neural Contraction Metrics (NCMs)☆32Updated 4 years ago
- Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad…☆70Updated 2 years ago
- Iterative Linear-Quadratic Games!☆152Updated 2 months ago
- ☆65Updated 4 months ago
- Safe Exploration with MPC and Gaussian process models☆88Updated 4 years ago
- Robot Controls Course Project☆53Updated 3 years ago
- Safe robot learning☆67Updated 2 months ago
- Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates☆70Updated last year
- ☆40Updated last year
- Sampling based Model Predictive Control package for Model-Based RL research☆51Updated 4 years ago
- Toolbox for Automated Controller Synthesis☆17Updated 9 months ago
- ☆29Updated 3 years ago