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

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.

[TRO 2025] NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning.

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

A curated list of robot social navigation.

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

[RA-L'25] An Reliable and Efficient Framework for Zero-Shot Object Navigation

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

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.

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

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

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.

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

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

Code base for SICNav T-RO paper and SICNav-Diffusion RA-L paper

Pedestrian ROS simulator with Gazebo and differential wheeled robots

RosNav-RL is a modular DRL framework for ROS 2 with a pluggable architecture, allowing you to switch between RL backends like Stable-Baselines3 and DreamerV3 to accelerate research and deployment.

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.

Deep Reinforcement Learning Based Mobile Robot Navigation Using ROS2 and Gazebo