Bolun Dai's Projects
The Reinforcement Learning environment for AI research in μRTS, a Real-time Strategy game simulator.
《Hello 算法》：动画图解、一键运行的数据结构与算法教程，支持 Java, C++, Python, Go, JS, TS, C#, Swift, Rust, Dart, Zig 等语言。
HouseExpo: A Large-scale 2D Indoor Layout Dataset
Your new Jekyll default theme.
Nonlinear MPC and WBC framework for legged robot based on OCS2 and ros-controls
This sofware is an open source framework used for didactics to simulate and deploy new robot plaforms
Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments
Manipulation Isaacs Benchmark (MIB) is a set of IsaacSim-based simulation environments for benchmarking learning-based robot manipultation algorithms.
Generate mazes of different shapes and arbitrary sizes using graph theory
Implementation of numerical solvers used in the Machines in Motion Laboratory
Self-contained, minimalistic implementation of diffusion models with Pytorch.
Simple and easily configurable grid world environments for reinforcement learning
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Demo scripts to quickly start with Crocoddyl
Simple and easily configurable 3D FPS-game-like environments for reinforcement learning
Code accompanying the paper "Learning Agile Robotic Locomotion Skills by Imitating Animals"
The code will soon be open source.
Examples for the mpi4py library
Pick and place using Simulink and MuJoCo
Masked Visual Pre-training for Robotics
Library for creating smooth cubic splines
Sailing Through Point Clouds: Safe Navigation Using Point Cloud Based Control Barrier Functions
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
State Constrained Stochastic Optimal Control for Continuous and Hybrid Dynamical Systems Using DFBSDE
Safe Navigation and Obstacle Avoidance Using Differentiable Optimization Based Control Barrier Functions
Data-Efficient Control Barrier Function Refinement
Learning a Better Control Barrier Function
State Constrained Stochastic Optimal Control Using LSTMs
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