Robot Navigation
130 papers with code • 4 benchmarks • 14 datasets
The fundamental objective of mobile Robot Navigation is to arrive at a goal position without collision. The mobile robot is supposed to be aware of obstacles and move freely in different working scenarios.
Benchmarks
These leaderboards are used to track progress in Robot Navigation
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Libraries
Use these libraries to find Robot Navigation models and implementationsDatasets
TAO
Gibson Environment
Habitat Platform
QDax
ISOD
Most implemented papers
SkiMap: An Efficient Mapping Framework for Robot Navigation
m4nh/skimap_ros
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We present a novel mapping framework for robot navigation which features a multi-level querying system capable to obtain rapidly representations as diverse as a 3D voxel grid, a 2. 5D height map and a 2D occupancy grid.
Path planning for Robotic Mobile Fulfillment Systems
This paper presents a collection of path planning algorithms for real-time movement of multiple robots across a Robotic Mobile Fulfillment System (RMFS).
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment.
Learning Representations Specialized in Spatial Knowledge: Leveraging Language and Vision
Here, we move one step forward in this direction and learn such representations by leveraging a task consisting in predicting continuous 2D spatial arrangements of objects given object-relationship-object instances (e. g., {``}cat under chair{''}) and a simple neural network model that learns the task from annotated images.
HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification
Compared to previous approaches, we use local memory units to efficiently leverage past temporal information and build a robust event-based representation.
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation
peteanderson80/Matterport3DSimulator
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ECCV 2018
In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices---We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task.
Offline and Online calibration of Mobile Robot and SLAM Device for Navigation
In the experiments, we confirm the parameters obtained by two types of offline calibration according to the degree of freedom of robot movement and validate the effectiveness of online correction method by plotting localized position error during robot's intense movement.
Predicting the Next Best View for 3D Mesh Refinement
Finding the best poses to capture part of the scene is one of the most challenging topic that goes under the name of Next Best View.
Learning a Representation Map for Robot Navigation using Deep Variational Autoencoder
For the navigation problem, we map the starting image and destination image to the latent space, then optimize a path on the learned manifold connecting the two points, and finally map the path back through decoder to a sequence of images.
Quality Diversity Through Surprise
Quality diversity is a recent family of evolutionary search algorithms which focus on finding several well-performing (quality) yet different (diversity) solutions with the aim to maintain an appropriate balance between divergence and convergence during search.
