https://github.com/yaaig-ufrgs/neuralfastdownward-fsm
공식 코드Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning
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/yimengli46/bellman_point_goal
공식 코드Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation
https://github.com/yohannnchen/afst
공식 코드https://github.com/yramon/LimeCounterfactual
공식 코드Counterfactual Explanation Algorithms for Behavioral and Textual Data
https://github.com/yumu-173/siamese-detr
공식 코드https://github.com/zjwzcx/GenNBV
공식 코드GenNBV: Generalizable Next-Best-View Policy for Active 3D Reconstruction
https://gitlab.com/2factor/QDSurprise
공식 코드https://gitlab.com/sustainable-continuum-monitoring/self-adaptive-moop
공식 코드An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
https://github.com/3dcvdeveloper/bgnet
https://github.com/BKAUTO/Trinocular-habitat-sim
https://github.com/Daffan/ros_jackal
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
https://github.com/DepthComputation/InSpaceType_Benchmark
https://github.com/GT-RIPL/robo-vln
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/MagnusCaligo/EvoCNN
Evolving Deep Convolutional Neural Networks for Image Classification
https://github.com/MapItAnywhere/MapItAnywhere
Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data
https://github.com/PathPlanning/GAN-Path-Finder
https://github.com/RoblabWh/RobLearn
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
https://github.com/StanfordVL/GibsonSim2RealChallenge
https://github.com/StanfordVL/iGibson
https://github.com/StanfordVL/iGibsonChallenge2021
Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
https://github.com/Sunnyzhr/habitat-lab
https://github.com/TomsonBoylett/Real-Time-Ellipse-Detection
https://github.com/USArmyResearchLab/ARL-SCOUT
Applying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue
https://github.com/abefetterman/hamstir-gym
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation
https://github.com/airi-institute/learn-to-follow
Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning
https://github.com/akanametov/PathGAN
https://github.com/alinamarcu/ruralscapes-nn
https://github.com/allenai/robothor-challenge
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/allenai/robustnav
PyRobot: An Open-source Robotics Framework for Research and Benchmarking
https://github.com/ayusefi/Localization-Papers
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
https://github.com/bpagare6/Struct2Depth-ImageFiles
https://github.com/bpagare6/Struct2Depth-Video
https://github.com/cfernandezlab/CFL
Corners for Layout: End-to-End Layout Recovery from 360 Images
https://github.com/cln515/HoloLensRobotNav
Offline and Online calibration of Mobile Robot and SLAM Device for Navigation
https://github.com/denkiwakame/python-ervo
L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation
https://github.com/ericchen321/ros_x_habitat
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.