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.
☆71Updated 3 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…☆178Updated 2 years ago
- Code needed to reproduce the examples found in "Learning Control Barrier Functions from Expert Demonstrations," by A. Robey, H. Hu, L. Li…☆73Updated 2 years ago
- Safe robot learning☆98Updated last year
- Adaptive control-oriented meta-learning for nonlinear systems☆69Updated 4 years ago
- Learning Lyapunov functions and control policies of nonlinear dynamical systems☆138Updated 4 years ago
- Synthesizing neural-network Lyapunov functions (and controllers) as stability certificate.☆170Updated 5 months ago
- Learning Certified Control Using Contraction Metric (CoRL 2020)☆32Updated 2 years ago
- Model-based Control using Koopman Operators☆55Updated 5 years ago
- Hamilton-Jacobi reachability analysis in JAX.☆156Updated last year
- ☆89Updated 2 years ago
- Lyapunov-stable Neural Control for State and Output Feedback☆78Updated last year
- Optimizing Dynamic Programming-Based Algorithms☆124Updated 2 months ago
- Robot Controls Course Project☆62Updated 4 years ago
- Automatic Tuning for Data-driven Model Predictive Control☆70Updated 2 years ago
- Toolkit for learning controllers based on robust control Lyapunov barrier functions☆174Updated last year
- ☆58Updated 3 years ago
- Soure code for Deep Koopman with Control☆94Updated 3 years ago
- Starter code accompanying homework assignments from AA203: Optimal and Learning-Based Control.☆27Updated 6 months ago
- ☆27Updated 4 years ago
- Repository for Koopman based learning and nonlinear control☆49Updated 2 years ago
- Code for the paper "Control Barriers in Bayesian Learning of System Dynamics"☆29Updated 3 years ago
- constraint differential dynamical programming☆28Updated 3 years ago
- Examples shown in class and recitation from AA203: Optimal and Learning-Based Control.☆53Updated 6 months ago
- ☆23Updated last year
- ☆30Updated 4 years ago
- Pytorch implementation of Model Predictive Control with learned models☆30Updated 5 years ago
- ☆80Updated 6 months ago
- Sampling based Model Predictive Control package for Model-Based RL research☆55Updated 5 years ago
- A python library for control from Signal Temporal Logic (STL) specifications☆50Updated 5 months ago
- This is the code repository for the DuSt-MPC paper, published at Robotics: Science and Systems (RSS) 2021.☆28Updated 4 years ago