SOTAVerified

Robot Navigation

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 542 papers

TitleStatusHype
ADA-DPM: A Neural Descriptors-based Adaptive Noise Point Filtering Strategy for SLAM—0
GeNIE: A Generalizable Navigation System for In-the-Wild Environments—0
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
Human-Robot Navigation using Event-based Cameras and Reinforcement Learning—0
Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material—0
Deep Equivariant Multi-Agent Control Barrier Functions—0
LLM-driven Indoor Scene Layout Generation via Scaled Human-aligned Data Synthesis and Multi-Stage Preference Optimization—0
Multimodal Spatial Language Maps for Robot Navigation and Manipulation—0
Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning—0
SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement LearningCode0
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All datasetsHabitat 2020 Object Nav minivalHabitat 2020 Object Nav test-stdHabitat 2020 Point Nav test-stdHabitat 2020 Point Nav minival

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VOSPL0.61—Unverified
2OccupancyAnticipationSPL0.6—Unverified
3DANSPL0.53—Unverified
4Visual Odometry for Realistic PointGoal NavigationSPL0.5—Unverified
5ego-localizationSPL0.31—Unverified
6Information BottleneckSPL0.13—Unverified
7AlstarSPL0.05—Unverified
8UCULabSPL0—Unverified
9RandomAgentSPL0—Unverified