openpilot is an operating system for robotics. Currently, it upgrades the driver assistance system on 275+ supported cars.

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.

Python sample codes and textbook for robotics algorithms.

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"

PyTorch 官方中文教程包含 60 分钟快速入门教程，强化教程，计算机视觉，自然语言处理，生成对抗网络，强化学习。欢迎 Star，Fork！

A library for differentiable nonlinear optimization

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.

Fast and simple implementation of RL algorithms, designed to run fully on GPU.

[ICCV 2023] VAD: Vectorized Scene Representation for Efficient Autonomous Driving

robotic motion planning library

古月学院课程代码

Optimal Motion Generation-tools: motion planning made easy

ViPlanner: Visual Semantic Imperative Learning for Local Navigation

PLUTO: Push the Limit of Imitation Learning-based Planning for Autonomous Driving

Generic Keyboard Teleop for ROS

从零手搓Flow Matching（Rectified Flow）

A model predictive controller for quadruped robots based on the single rigid body model and written in python. Gradient-based (acados) or Sampling-based (jax).

[TNNLS] Differentiable Integrated Prediction and Planning Framework for Urban Autonomous Driving

An Inkscape extension: Latex/Tex editor for Inkscape

An optimization framework that links CasADi, Ipopt, ACADOS and biorbd for Optimal Control Problem

End-to-End Navigation with VLMs

A ROS package that makes picking and placing objects with MoveIt and GraspIt, easier than ever! 🤖

Code release for ICLR 2023 paper "NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning"

Reinforcement Learning-based Hybrid Policy Path Planning in Diverse Parking Scenarios

On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions