phuongboi

Python-RVO2

Optimal Reciprocal Collision Avoidance, Python bindings

CrowdNav_DSRNN

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

CrowdNav

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

CADRL

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 NO LONGER MAINTAINED.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

motion-planner-reinforcement-learning

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

quadcopter_sim

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

rl-pendulum

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

drone_controls

drone controls

high_mpc

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

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.

PPO-PyTorch

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

gym-pybullet-drones

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

quad-ctrl

Quadcopter Controller with Deep Reinforcement Learning

sjtu_drone

ROS/ ROS 2 Gazebo quadcopter simulator.

Reinforcement-Learning-Lunar_Lander

lunar-lander

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

tut20_people_avoidance

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.

FastSLAM

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

visual-pushing-grasping

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

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.

slam

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

3dv_tutorial

An Invitation to 3D Vision: A Tutorial for Everyone

playing-atari-with-deep-RL