Stars
[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型
Reinforcement Learning-based Hybrid Policy Path Planning in Diverse Parking Scenarios
MinRL provides clean, minimal implementations of fundamental reinforcement learning algorithms in a customizable GridWorld environment. The project focuses on educational clarity and implementation…
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning
[NeurIPS 2024] A Generalizable World Model for Autonomous Driving
DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving
Collect some World Models for Autonomous Driving (and Robotic) papers.
Python Implementation of Reinforcement Learning: An Introduction
强化学习中文教程(蘑菇书🍄),在线阅读地址:https://datawhalechina.github.io/easy-rl/
Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)
Python implementation of a bunch of multi-robot path-planning algorithms.
Common used path planning algorithms with animations.
Python sample codes and textbook for robotics algorithms.
We use hybrid a star and optimization-based method for trajectory planning of the autonomous vehicle parking
Python implementation of an automatic parallel parking system in a virtual environment, including path planning, path tracking, and parallel parking
直播源相关资源汇总 📺 💯 IPTV、M3U —— 勤洗手、戴口罩,祝愿所有人百毒不侵
刷算法全靠套路,认准 labuladong 就够了!English version supported! Crack LeetCode, not only how, but also why.
Visual Studio Code Dark Plus theme for JetBrains IDEs
Awesome Curated List of Eye Gaze Estimation Paper
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
Implementation of PFLD A Practical Facial Landmark Detector , reference to https://arxiv.org/pdf/1902.10859.pdf
This is an official implementation of facial landmark detection for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919