Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

[RSS2024] Official implementation of "Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation"

[IEEE RA-L'25] NavRL: Learning Safe Flight in Dynamic Environments (NVIDIA Isaac/Python/ROS1/ROS2)

[T-RO 2023] DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles

A curated list of robot social navigation.

Wild Visual Navigation: A system for fast traversability learning via pre-trained models and online self-supervision

Implementation of the D* lite algorithm in Python for "Improved Fast Replanning for Robot Navigation in Unknown Terrain"

A goal-driven autonomous exploration through deep reinforcement learning (ICRA 2022) system that combines reactive and planned robot navigation in unknown environments

Indoor segmentation for robot navigating, which is based on deeplab model in TensorFlow.

Deep Reinforcement Learning for mobile robot navigation in IR-SIM simulation. Using DRL (SAC, TD3, PPO, DDPG) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

Repository for the paper "Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues"

[IROS23] Hybrid Map-Based Path Planning for Robot Navigation in Unstructured Environments

Deep Reinforcement Learning for mobile robot navigation in ROS2 Gazebo simulator. Using DRL (SAC, TD3) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

Pedestrian ROS simulator with Gazebo and differential wheeled robots

This repository contains the source code for our paper: "NaviSTAR: Socially Aware Robot Navigation with Hybrid Spatio-Temporal Graph Transformer and Preference Learning". For more details, please refer to our project website at https://sites.google.com/view/san-navistar.

Code base for SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization

Simple differential drive robot for indoor environments simulated using ROS and Gazebo.

A Reinforcement Learning (RL) based navigation implementation for mobile robot navigation. Q-Learning, SARSA and Deep Q-Network algorithms were compared.

Adaptive Risk Tendency Implicit Quantile Network for Drone Navigation under Partial Observability.

RosNav-RL: A flexible, modular framework for building, training, and deploying reinforcement learning navigation agents in ROS. Supports multiple RL backends with ready-to-use components for rapid development and experimentation in autonomous robot navigation.