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Multi-Joint dynamics with Contact. A general purpose physics simulator.
State-of-the-art method for model-based dexterous manipulation
Python bindings for real-time control of Franka Emika robots.
Rory-WeiCheng / dairlib
Forked from DAIRLab/dairlibForked from DAIRLab/dairlib, for C3 adaptive MPC experiments. Switch to admm_adaptive branch for current progress
A curated list of awesome computer vision resources
Lecture Slides, Personal Notes, Homework Solutions and Codes for ESE 619: Model Predictive Control 2023 Spring @ UPenn
😎 A curated list of awesome collision detection libraries and resources
We developed a task-driven hybrid model reduction method for solving dexterous manipulation with 5 minutes of online learning.
CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained …
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.
Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning 5BD3 and control tasks.
wanxinjin / Learning-LCS
Forked from DAIRLab/Learning-LCSA new learning formulation/method to learn/identify a piecewise linear system, also named as linear complementarity system.
A new learning formulation/method to learn/identify a piecewise linear system, also named as linear complementarity system.
A new method for a robot to learn a control objective from human user's directional corrections.
Inverse optimal control from incomplete trajectory observations, proposing the concept of the recovery matrix which provides further insights into objective learning process.
A framework and method to jointly learn a (neural) control objective function and a time-warping function only from sparse demonstrations or waypoints.