PointGoal Navigation
14 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in PointGoal Navigation
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Libraries
Use these libraries to find PointGoal Navigation models and implementations
Habitat Platform
Latest papers
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation
yimengli46/bellman_point_goal
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In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem.
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement
facebookresearch/habitat-lab
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Specifically, the Pick skill involves a robot picking an object from a table.
Is Mapping Necessary for Realistic PointGoal Navigation?
rpartsey/pointgoal-navigation
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CVPR 2022
However, for PointNav in a realistic setting (RGB-D and actuation noise, no GPS+Compass), this is an open question; one we tackle in this paper.
Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation
fpv-iplab/mid-level-fusion-nav
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All the proposed navigation models have been trained with the Habitat simulator on a synthetic office environment and have been tested on the same real-world environment using a real robotic platform.
Realistic PointGoal Navigation via Auxiliary Losses and Information Bottleneck
nicogrande/habitat-pointnav-via-ib
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Under this setting, the agent incurs a penalty for using this privileged information, encouraging the agent to only leverage this information when it is crucial to learning.
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
facebookresearch/habitat-challenge
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When compared to existing photorealistic 3D datasets such as Replica, MP3D, Gibson, and ScanNet, images rendered from HM3D have 20 - 85% higher visual fidelity w. r. t.
Out of the Box: Embodied Navigation in the Real World
In this work, we detail how to transfer the knowledge acquired in simulation into the real world.
Large Batch Simulation for Deep Reinforcement Learning
shacklettbp/bps-nav
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ICLR 2021
We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19, 000 frames of experience per second on a single GPU and up to 72, 000 frames per second on a single eight-GPU machine.
Auxiliary Tasks Speed Up Learning PointGoal Navigation
joel99/habitat-pointnav-aux
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PointGoal Navigation is an embodied task that requires agents to navigate to a specified point in an unseen environment.
Learning to Explore using Active Neural SLAM
devendrachaplot/Neural-SLAM
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ICLR 2020
The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies).
