1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Space exploration, trading, and combat game.
🎈 Simple reactive notebooks for Julia
Visualize and compare datasets, target values and associations, with one line of code.
The purpose of this project is to share knowledge on how awesome Streamlit is and can be
A game of lonely space adventure
[ICML 2017] TensorFlow code for Curiosity-driven Exploration for Deep Reinforcement Learning
Naev has moved to codeberg! This is just a mirror.
Pytorch code for ICLR-20 Paper "Learning to Explore using Active Neural SLAM"
[CMU] A Versatile and Modular Framework Designed for Autonomous Unmanned Aerial Vehicles [UAVs] (C++/ROS/PX4)
A curated list of awesome exploration RL resources (continually updated)
A ROS package that implements a multi-robot RRT-based map exploration algorithm. It also has the image-based frontier detection that uses image processing to extract frontier points.
Pytorch code for NeurIPS-20 Paper "Object Goal Navigation using Goal-Oriented Semantic Exploration"
This is the pytorch implementation of Hindsight Experience Replay (HER) - Experiment on all fetch robotic environments.
[IEEE T-RO 2023] A modularized multi-robot SLAM system with elevation mapping and a costmap converter for easy navigation. Different odometry and loop closure algorithms can be easily integrated into the system.
This repository primarily organizes papers, code, and other relevant materials related to Active SLAM and Robotic Exploration.
A goal-driven autonomous exploration through deep reinforcement learning (ICRA 2022) system that combines reactive and planned robot navigation in unknown environments
This work contains a modified version of cartographer_frontier_detection and rrt_exploration. We implement an active exploration process and improve its robustness and performance. More details are described in the paper "Frontier Detection and Reachability Analysis for Efficient 2D Graph-SLAM Based Active Exploration" (IROS2020).
[CVPR 2026 (Oral)] MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping