Papers with Code 아카이브
Robot Navigation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jmeadows17/equational-inference
공식 코드https://github.com/joel99/habitat-pointnav-aux
공식 코드https://github.com/joel99/objectnav
공식 코드https://github.com/kailaisun/indoor-depth-completion
공식 코드A Two-Stage Masked Autoencoder Based Network for Indoor Depth Completion
https://github.com/kasunweerkoon/VAPOR
공식 코드VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning
https://github.com/knwoo/e2map
공식 코드E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models
https://github.com/kojirotakeyama/locovr
공식 코드LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality
https://github.com/kt2024-hal/locovr
공식 코드LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality
https://github.com/kurorororo/didp-models
공식 코드https://github.com/l-scofano/sda
공식 코드https://github.com/lacava/gecco_2019
공식 코드Semantic variation operators for multidimensional genetic programming
https://github.com/lasmp23/lasmp
공식 코드https://github.com/lemonqc/kg2text
공식 코드Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency
https://github.com/leomingyangli/semanticslam
공식 코드SemanticSLAM: Learning based Semantic Map Construction and Robust Camera Localization
https://github.com/levilelis/h-levin
공식 코드https://github.com/lhrlab/kbqa-o1
공식 코드KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search
https://github.com/luca-morreale/stochastic-nbv-4-mesh-refinement
공식 코드https://github.com/lzk23/hpcschedule
공식 코드https://github.com/m4nh/skimap_ros
공식 코드https://github.com/manglanisagar/vision-search-navigation
공식 코드Real-time Vision-based Navigation for a Robot in an Indoor Environment
https://github.com/mark12ding/sam2long
공식 코드SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree
https://github.com/maximecb/gym-miniworld
공식 코드Towards real-world navigation with deep differentiable planners
https://github.com/maytusp/tta-nav
공식 코드TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation under Visual Corruptions
https://github.com/mchancan/citylearn
공식 코드MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation
https://github.com/mchancan/deepseqslam
공식 코드DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
https://github.com/mehrab-abrar/Sim2Real
공식 코드An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a Grid
https://github.com/mengmengliu1998/gatraj
공식 코드GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model
https://github.com/merschformann/RAWSim-O
공식 코드https://github.com/mszuyx/mosts
공식 코드https://github.com/nedzhaken/socsarl-ol
공식 코드https://github.com/netmanaiops/psqueeze
공식 코드Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems
https://github.com/nicogrande/habitat-pointnav-via-ib
공식 코드Realistic PointGoal Navigation via Auxiliary Losses and Information Bottleneck
https://github.com/nikeke19/safe-mult-rl
공식 코드A Multiplicative Value Function for Safe and Efficient Reinforcement Learning
https://github.com/nkotani/pointanywhere
공식 코드Point Anywhere: Directed Object Estimation from Omnidirectional Images
https://github.com/northocean/msn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.