wanxinjin / Safe-PDPLinks
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.
☆70Updated 2 years ago
Alternatives and similar repositories for Safe-PDP
Users that are interested in Safe-PDP are comparing it to the libraries listed below
Sorting:
- A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, contr…☆175Updated last year
- Robot Controls Course Project☆59Updated 3 years ago
- Learning Certified Control Using Contraction Metric (CoRL 2020)☆30Updated 2 years ago
- Learning Lyapunov functions and control policies of nonlinear dynamical systems☆134Updated 4 years ago
- ☆87Updated 2 years ago
- Optimizing Dynamic Programming-Based Algorithms☆117Updated 2 weeks ago
- Robust Online Motion Planning using Contraction Theory☆58Updated 6 years ago
- Model-based Control using Koopman Operators☆51Updated 5 years ago
- Repository for Koopman based learning and nonlinear control☆43Updated 2 years ago
- ☆56Updated 3 years ago
- Code needed to reproduce the examples found in "Learning Control Barrier Functions from Expert Demonstrations," by A. Robey, H. Hu, L. Li…☆67Updated last year
- Lyapunov-stable Neural Control for State and Output Feedback☆69Updated 11 months ago
- Toolkit for learning controllers based on robust control Lyapunov barrier functions☆165Updated 11 months ago
- ☆24Updated 4 years ago
- Code for the paper "Control Barriers in Bayesian Learning of System Dynamics"☆28Updated 2 years ago
- Safe robot learning☆91Updated 7 months ago
- constraint differential dynamical programming☆29Updated 2 years ago
- Colab notebooks showcasing experiments on MPPI (model predictive path integral control) and CBF (control barrier function). Utilizes jax …☆78Updated 4 months ago
- Synthesizing neural-network Lyapunov functions (and controllers) as stability certificate.☆161Updated 3 weeks ago
- Hamilton-Jacobi reachability analysis in JAX.☆138Updated 7 months ago
- Soure code for Deep Koopman with Control☆85Updated 3 years ago
- Codes for designing Neural Contraction Metrics (NCMs)☆33Updated 4 years ago
- ☆69Updated last year
- Neural Koopman Lyapunov Control☆25Updated 2 years ago
- Automatic Tuning for Data-driven Model Predictive Control☆64Updated 2 years ago
- Sampling based GP MPC☆28Updated this week
- Papers on Safety Critical Controls using Control Barrier Functions☆39Updated 7 months ago
- Get started with Reachability-based Trajectory Design for static obstacles☆62Updated 3 years ago
- Starter code accompanying homework assignments from AA203: Optimal and Learning-Based Control.☆25Updated last month
- A simple 1d simulator for the "Neural-Lander" paper, ICRA 2019☆17Updated 2 years ago