Robot Navigation

115 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


Use these libraries to find Robot Navigation models and implementations

Most implemented papers

Habitat: A Platform for Embodied AI Research

facebookresearch/habitat-sim ICCV 2019

We present Habitat, a platform for research in embodied artificial intelligence (AI).

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

facebookresearch/habitat-api ICLR 2020

We leverage this scaling to train an agent for 2. 5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs.

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

vita-epfl/DyNav 24 Sep 2018

We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework.

Gibson Env: Real-World Perception for Embodied Agents

StanfordVL/GibsonEnv CVPR 2018

Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly.

Corners for Layout: End-to-End Layout Recovery from 360 Images

cfernandezlab/CFL 19 Mar 2019

The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade.

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

RoblabWh/RobLearn 28 May 2020

In this paper we present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner.

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

gnns4hri/sngnnv2 17 Feb 2021

This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms.

A Fast Ellipse Detector Using Projective Invariant Pruning

dlut-dimt/ellipse-detector 26 Aug 2016

Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis where the detection time is always a critical issue.

Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

gkahn13/gcg 29 Sep 2017

To address the need to learn complex policies with few samples, we propose a generalized computation graph that subsumes value-based model-free methods and model-based methods, with specific instantiations interpolating between model-free and model-based.