piliwilliam0306

system-design-101

Explain complex systems using visuals and simple terms. Help you prepare for system design interviews.

cleanrl

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

AI-Job-Notes

AI算法岗求职攻略（涵盖准备攻略、刷题指南、内推和AI公司清单等资料）

DeepRL

Modularized Implementation of Deep RL Algorithms in PyTorch

IsaacGymEnvs

Isaac Gym Reinforcement Learning Environments

Visual_SLAM_Related_Research

视觉(语义) SLAM 相关研究跟踪

ros_best_practices

Best practices, conventions, and tricks for ROS

StanfordQuadruped

betterpython

Code examples for my Write Better Python Code series on YouTube.

legged_gym

Isaac Gym Environments for Legged Robots

CppNumericalSolvers

a lightweight header-only C++17 library of numerical optimization methods for nonlinear functions based on Eigen

rl_games

RL implementations

OmniIsaacGymEnvs

Reinforcement Learning Environments for Omniverse Isaac Gym

mpc_ros

Differential Wheeled Mobile Robot - Nonlinear Model Predictive Control based on ROS

cracking-the-coding-interview

:books: C++ and Python solutions with automated tests for Cracking the Coding Interview 6th Edition.

gymnax

RL Environments in JAX 🌍

skrl

Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Omniverse Isaac Gym and Isaac Lab

tmrl

Reinforcement Learning for real-time applications - host of the TrackMania Roborace League

OmniGibson

OmniGibson: a platform for accelerating Embodied AI research built upon NVIDIA's Omniverse engine. Join our Discord for support: https://discord.gg/bccR5vGFEx

robot_calibration

Generic calibration for robots

assistive-gym

Assistive Gym, a physics-based simulation framework for physical human-robot interaction and robotic assistance.

cpp_software_design

Code Examples from "C++ Software Design: Design Principles and Patterns for High-Quality Software" (ISBN: 1098113160)

VO-SLAM-Review

SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects

go2_omniverse

Unitree Go2, Unitree G1 support for Nvidia Isaac Lab (Isaac Gym / Isaac Sim)

turtlebot3_drlnav

A ROS2-based framework for TurtleBot3 DRL autonomous navigation

rclUE

flatland

A 2D robot simulator for ROS

turtlebot3-UE

nav-gym

A OpenAI-gym compatible navigation simulator for mobile robot navigation

SocNavGym