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
Habitat: A Platform for Embodied AI Research
facebookresearch/habitat-sim
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ICCV 2019
We present Habitat, a platform for research in embodied artificial intelligence (AI).
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Models and examples built with TensorFlow
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
facebookresearch/habitat-api
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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
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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
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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
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
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
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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
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
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.
