PointGoal Navigation
14 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in PointGoal Navigation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find PointGoal Navigation models and implementations
Habitat Platform
Latest papers with no code
MOPA: Modular Object Navigation with PointGoal Agents
We propose a simple but effective modular approach MOPA (Modular ObjectNav with PointGoal agents) to systematically investigate the inherent modularity of the object navigation task in Embodied AI.
Emergence of Maps in the Memories of Blind Navigation Agents
A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial.
Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment
To improve the PointGoal navigation accuracy without GPS signal, we use visual odometry (VO) and propose a novel action integration module (AIM) trained in unsupervised manner.
Embodied Navigation at the Art Gallery
This feature is challenging for occupancy-based agents which are usually trained in crowded domestic environments with plenty of occupancy information.
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge
Recent advances in deep reinforcement learning and scalable photorealistic simulation have led to increasingly mature embodied AI for various visual tasks, including navigation.
The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation
It is fundamental for personal robots to reliably navigate to a specified goal.
GridToPix: Training Embodied Agents with Minimal Supervision
While deep reinforcement learning (RL) promises freedom from hand-labeled data, great successes, especially for Embodied AI, require significant work to create supervision via carefully shaped rewards.
THDA: Treasure Hunt Data Augmentation for Semantic Navigation
We show that this is a natural consequence of optimizing for the task metric (which in fact penalizes exploration), is enabled by powerful observation encoders, and is possible due to the finite set of training environment configurations.
How to Train PointGoal Navigation Agents on a (Sample and Compute) Budget
PointGoal navigation has seen significant recent interest and progress, spurred on by the Habitat platform and associated challenge.
Bi-directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents
Simulation offers the ability to train large numbers of robots in parallel, and offers an abundance of data.
