Bin Hu's repositories
asif
CBF library used in a wide variety of simulation and hardware implementations. Embedded friendly, requires OSQP. Library originally written by Thomas Gurriet. Contains slightly modified version of libaffa, found here: https://github.com/ogay/libaffa
awesome-neural-ode
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
CERLAB-UAV-Autonomy
[CMU] A Versatile and Modular Framework Designed for Autonomous Unmanned Aerial Vehicles [UAVs] (C++/ROS/PX4)
darts
Differentiable architecture search for convolutional and recurrent networks
DeepRL
Modularized Implementation of Deep RL Algorithms in PyTorch
diffusion_policy
[RSS 2023] Diffusion Policy Visuomotor Policy Learning via Action Diffusion
faster
3D Trajectory Planner in Unknown Environments
GCOPTER
A General-Purpose Trajectory Optimizer for Multicopters
Kimera-Multi
Index repo for Kimera-Multi system
LIO-SAM
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
LVI-SAM
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
mav_trajectory_generation
Polynomial trajectory generation and optimization, especially for rotary-wing MAVs.
onboard_detector
[IEEE RA-L'23] Dynamic Obstacle Detection and Tracking (DODT) algorithm for Autonomous Robots (C++/ROS)
osqp
The Operator Splitting QP Solver
pgm-spring-2019
Course webpage for PGM, Spring 2019.
quadrotor
Quadrotor control, path planning and trajectory optimization
reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
slam_in_autonomous_driving
《自动驾驶中的SLAM技术》对应开源代码
tiny-training
On-Device Training Under 256KB Memory [NeurIPS'22]
TOG
Real-time object detection is one of the key applications of deep neural networks (DNNs) for real-world mission-critical systems. While DNN-powered object detection systems celebrate many life-enriching opportunities, they also open doors for misuse and abuse. This project presents a suite of adversarial objectness gradient attacks, coined as TOG,