Visual Navigation
107 papers with code • 6 benchmarks • 16 datasets
Visual Navigation is the problem of navigating an agent, e.g. a mobile robot, in an environment using camera input only. The agent is given a target image (an image it will see from the target position), and its goal is to move from its current position to the target by applying a sequence of actions, based on the camera observations only.
Source: Vision-based Navigation Using Deep Reinforcement Learning
Benchmarks
These leaderboards are used to track progress in Visual Navigation
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Libraries
Use these libraries to find Visual Navigation models and implementations
Replica
AI2-THOR
SUNCG
R2R
2D-3D-S
AVD
HELP
MINOS
Talk the Walk
HM3DSem
Latest papers
Towards Disturbance-Free Visual Mobile Manipulation
In this paper, we study the problem of training agents to complete the task of visual mobile manipulation in the ManipulaTHOR environment while avoiding unnecessary collision (disturbance) with objects.
Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds
robot-learning-freiburg/Dav-Nav
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Audio-visual navigation combines sight and hearing to navigate to a sound-emitting source in an unmapped environment.
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning
kibeomkim/gace-gdan
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NeurIPS 2021
Learning in a multi-target environment without prior knowledge about the targets requires a large amount of samples and makes generalization difficult.
SGoLAM: Simultaneous Goal Localization and Mapping for Multi-Object Goal Navigation
eunsunlee/sgolam
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We present SGoLAM, short for simultaneous goal localization and mapping, which is a simple and efficient algorithm for Multi-Object Goal navigation.
Waypoint Models for Instruction-guided Navigation in Continuous Environments
jacobkrantz/VLN-CE
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ICCV 2021
Little inquiry has explicitly addressed the role of action spaces in language-guided visual navigation -- either in terms of its effect on navigation success or the efficiency with which a robotic agent could execute the resulting trajectory.
SeanNet: Semantic Understanding Network for Localization Under Object Dynamics
This paper proposes a SEmantic understANding Network (SeanNet) architecture that enables an effective learning process with coupled visual and semantic inputs.
Towards Autonomous Crop-Agnostic Visual Navigation in Arable Fields
Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring to weed management and fertilizer application.
Towards real-world navigation with deep differentiable planners
shuishida/calvin
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CVPR 2022
To avoid the potentially hazardous trial-and-error of reinforcement learning, we focus on differentiable planners such as Value Iteration Networks (VIN), which are trained offline from safe expert demonstrations.
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals.
Collaborative Visual Navigation
Haiyang-W/MAVN
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As a fundamental problem for Artificial Intelligence, multi-agent system (MAS) is making rapid progress, mainly driven by multi-agent reinforcement learning (MARL) techniques.
