wanxinjin / Pontryagin-Differentiable-Programming
A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.
☆158Updated 11 months ago
Related projects: ⓘ
- Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad…☆66Updated 2 years ago
- Toolkit for learning controllers based on robust control Lyapunov barrier functions☆126Updated 2 months ago
- Synthesizing neural-network Lyapunov functions (and controllers) as stability certificate.☆138Updated 8 months ago
- Learning Lyapunov functions and control policies of nonlinear dynamical systems☆117Updated 3 years ago
- Soure code for Deep Koopman with Control☆64Updated 2 years ago
- Optimizing Dynamic Programming-Based Algorithms☆99Updated last month
- MPC with Gaussian Process☆193Updated 5 years ago
- Use PyTorch Models with CasADi and Acados☆192Updated 10 months ago
- Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gy…☆126Updated last year
- ☆77Updated last year
- Safe Exploration with MPC and Gaussian process models☆87Updated 4 years ago
- Lyapunov-stable Neural Control for State and Output Feedback☆53Updated 2 months ago
- ☆236Updated last year
- A Model Predictive Control (MPC) Python library based on the OSQP solver.☆185Updated 3 years ago
- Hamilton-Jacobi reachability analysis in JAX.☆103Updated 5 months ago
- Model-based Control using Koopman Operators☆45Updated 4 years ago
- The reimplementation of Model Predictive Path Integral (MPPI) from the paper "Information Theoretic MPC for Model-Based Reinforcement Lea…☆88Updated 4 years ago
- Pytorch version of the MPC in model-based reinforcement learning (MBRL), currently only test in the CartPole-swing-up environment☆72Updated 4 years ago
- Matlab Interface for Control Barrier Function (CBF) and Control Lyapunov Function (CLF) based control methods.☆248Updated 2 months ago
- Codes for designing Neural Contraction Metrics (NCMs)☆32Updated 4 years ago
- Open-source implementation of Deep Lagrangian Networks (DeLaN)☆76Updated 2 years ago
- Safe robot learning☆61Updated 2 weeks ago
- Inverse optimal control from incomplete trajectory observations, proposing the concept of the recovery matrix which provides further insi…☆12Updated 3 years ago
- ☆53Updated last month
- A toolbox for trajectory optimization of dynamical systems☆50Updated 2 years ago
- ☆61Updated 2 months ago
- Robust Online Motion Planning using Contraction Theory☆53Updated 5 years ago
- Real-time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms☆155Updated last year
- Use PyTorch Models with CasADi for data-driven optimization or learning-based optimal control. Supports Acados.☆336Updated last week
- HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problem…☆157Updated 10 months ago