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.

Source: Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding

Benchmarks

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These leaderboards are used to track progress in Robot Navigation

Dataset	Best Model	Compare
Habitat 2020 Object Nav test-std	THDA	See all
Habitat 2020 Point Nav test-std	VO	See all
Habitat 2020 Point Nav minival	VO	See all
Habitat 2020 Object Nav minival	SemExp	See all

Libraries

Use these libraries to find Robot Navigation models and implementations

facebookresearch/habitat-challenge

3 papers

292

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators

wang-chen/correlation_flow • 20 Feb 2018

Robust velocity and position estimation is crucial for autonomous robot navigation.

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PyRobot: An Open-source Robotics Framework for Research and Benchmarking

facebookresearch/pyrobot • 19 Jun 2019

This paper introduces PyRobot, an open-source robotics framework for research and benchmarking.

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Code

Object Goal Navigation using Goal-Oriented Semantic Exploration

devendrachaplot/Object-Goal-Navigation • • NeurIPS 2020

We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category.

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Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation

onorabil/frameSegmentation • 2 Oct 2020

Motivated by the lack of a large video aerial dataset, we also introduce Ruralscapes, a new dataset with high resolution (4K) images and manually-annotated dense labels every 50 frames - the largest of its kind, to the best of our knowledge.

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Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning

StanfordVL/iGibsonChallenge2021 • • 16 Oct 2020

Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes.

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Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning

Shuijing725/CrowdNav_DSRNN • • 9 Nov 2020

Safe and efficient navigation through human crowds is an essential capability for mobile robots.

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End-to-End Egospheric Spatial Memory

ivy-dl/memory • • 15 Feb 2021

Spatial memory, or the ability to remember and recall specific locations and objects, is central to autonomous agents' ability to carry out tasks in real environments.

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