phuongboi

ModernRobotics

Modern Robotics: Mechanics, Planning, and Control Code Library --- The primary purpose of the provided software is to be easy to read and educational, reinforcing the concepts in the book. The code is optimized neither for efficiency nor robustness.

PPO-PyTorch

Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

3dv_tutorial

An Invitation to 3D Vision: A Tutorial for Everyone

gym-pybullet-drones

PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control

visual-pushing-grasping

Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.

CrowdNav

[ICRA19] Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

high_mpc

Policy Search for Model Predictive Control with Application to Agile Drone Flight

motion-planner-reinforcement-learning

End to end motion planner using Deep Deterministic Policy Gradient (DDPG) in gazebo

Python-RVO2

Optimal Reciprocal Collision Avoidance, Python bindings

CrowdNav_DSRNN

[ICRA 2021] Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning

CADRL

Implementation of paper "Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning". NO LONGER MAINTAINED. CHECK OUT CrowdNav.

sjtu_drone

ROS/ ROS 2 Gazebo quadcopter simulator.

Training-Neural-Networks-for-Event-Based-End-to-End-Robot-Control

TUM Master’s thesis: Steering a robot with an event-based vision sensor in a lane-keeping task using methods such as Deep Reinforcement Learning or Spiking Neural Networks.

Reinforcement-Learning-Lunar_Lander

LearningAgileFlight_SE3

Learning Agile Flights on SE(3): a novel deep SE(3) motion planning and control method for quadrotors. It learns an MPC's adaptive SE(3) decision variables parameterized by a portable DNN, encouraging the quadrotor to fly through the gate with maximum safety margins under diverse settings.

lunar-lander

Implementation of deep reinforcement learning algorithms for training an agent to play the game lunar lander

quadcopter_sim

simulation of quadcopter using pybullet (calculations) and pyqtgraph (visualisation)

quad-ctrl

Quadcopter Controller with Deep Reinforcement Learning

rl-pendulum

Open AI Gym - Pendulum-v1 reinforcement learning (DQN, SAC)

drone_controls

drone controls

FastSLAM

This project implements grid-based FastSLAM1.0 and FastSLAM2.0 algorithms to solve SLAM problem in a simulated environment.

playing-atari-with-deep-RL

tut20_people_avoidance

slam

Simultaneous Localization and Mapping in V-REP robotics simulator using LIDAR data.