Papers with Code 아카이브
Visual Navigation
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Visual Navigation is the problem of navigating an agent, e.g. a mobile robot, in an environment using camera input only. The agent is given a target image (an image it will see from the target position), and its goal is to move from its current position to the target by applying a sequence of actions, based on the camera observations only.
Source: Vision-based Navigation Using Deep Reinforcement Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/feedzai/research-arms
공식 코드https://github.com/fudan-zvg/openocc
공식 코드OpenOcc: Open Vocabulary 3D Scene Reconstruction via Occupancy Representation
https://github.com/gengzezhou/navgpt
공식 코드NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models
https://github.com/gengzezhou/same
공식 코드SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts
https://github.com/google-research/valan
공식 코드Environment-agnostic Multitask Learning for Natural Language Grounded Navigation
https://github.com/habicrowd/HabiCrowd
공식 코드HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation
https://github.com/hanqingwangai/vxn
공식 코드https://github.com/harvard-edge/airlearning
공식 코드Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation
https://github.com/hengli/camodocal
공식 코드https://github.com/hren20/naivibridger
공식 코드Prior Does Matter: Visual Navigation via Denoising Diffusion Bridge Models
https://github.com/huangdiudiu/eqv
공식 코드https://github.com/igashov/retrobridge
공식 코드https://github.com/insait-institute/dager-gradient-inversion
공식 코드https://github.com/intelligolabs/Le-RNR-Map
공식 코드Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language
https://github.com/ivy-dl/memory
공식 코드https://github.com/jacobkrantz/VLN-CE
공식 코드Waypoint Models for Instruction-guided Navigation in Continuous Environments
https://github.com/jakob-bach/alternative-feature-selection
공식 코드Finding Optimal Diverse Feature Sets with Alternative Feature Selection
https://github.com/jbwasse2/sling
공식 코드https://github.com/jennyzzt/good_time_to_ask
공식 코드Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation
https://github.com/jiayouqin/h-splitter
공식 코드Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation
https://github.com/jkulhanek/a2cat-vn-pytorch
공식 코드https://github.com/jkulhanek/dmlab-vn
공식 코드Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning
https://github.com/jkulhanek/robot-visual-navigation
공식 코드Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning
https://github.com/jmeadows17/equational-inference
공식 코드https://github.com/joel99/objectnav
공식 코드https://github.com/jxwuyi/HouseNavAgent
공식 코드https://github.com/khanhptnk/hanna
공식 코드https://github.com/kibeomkim/gace-gdan
공식 코드Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning
https://github.com/kkelchte/fgbg
공식 코드RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues
https://github.com/kurorororo/didp-models
공식 코드https://github.com/lacava/gecco_2019
공식 코드Semantic variation operators for multidimensional genetic programming
https://github.com/lasuomela/carla_vloc_benchmark
공식 코드https://github.com/lavi-lab/navillm
공식 코드https://github.com/lemonqc/kg2text
공식 코드Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency
https://github.com/levilelis/h-levin
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.