[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.

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

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.

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

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 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.

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.

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

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

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

(IJRR) Mixed Strategy Nash Equilibrium for Crowd Navigation

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

Mobile Robot Planner with Low-cost Cameras Using Deep Reinforcement Learning

Code for SegMASt3R: Geometry Grounded Segment Matching (NeurIPS 2025)

DRL-VO navigation policy for BARN Challenge

[TRO-2025] SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation

Socially normative mobile robot navigation

(RSS 2021) Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation