computer-vision-5
Robot Navigation
3260 papers • 126 benchmarks • 313 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
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in robot-navigation-10
Trend
Dataset
Best Model
Actions
Habitat 2020 Object Nav test-std
Habitat 2020 Object Nav test-std
Habitat 2020 Point Nav test-std
Habitat 2020 Point Nav test-std
Libraries
iUse these libraries to find robot-navigation-10 models and implementations
3 papers 300
Datasets
Subtasks
Most implemented papers
Habitat: A Platform for Embodied AI Research
Devi Parikh, Dhruv Batra, V. Koltun, Julian Straub, Jitendra Malik, M. Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Jia Liu•Mon Apr 01 2019
The comparison between learning and SLAM approaches from two recent works are revisited and evidence is found -- that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and the first cross-dataset generalization experiments are conducted.
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