https://github.com/ram81/il_rl_baselines
PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav
Papers with Code 아카이브
Robot Navigation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/ray-project/ray/tree/master/rllib
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/rob-tang/rob-tang.github.io
Detecting danger in gridworlds using Gromov's Link Condition
https://github.com/robot-love/depth_from_video_in_the_wild
https://github.com/romi514/TLSGAN
https://github.com/s-gupta/map-plan-baseline
https://github.com/sair-lab/ni-slam
Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
https://github.com/sepsamavi/safe-interactive-crowdnav
https://github.com/sisl/CMSSPs.jl
Hybrid Planning for Dynamic Multimodal Stochastic Shortest Paths
https://github.com/snasiriany/leap
https://github.com/snt-arg/lidar_situational_graphs
Situational Graphs for Robot Navigation in Structured Indoor Environments
https://github.com/super5-22/habitat-vis
https://github.com/tensorflow/models/tree/master/research/struct2depth
https://github.com/tmralmeida/thor-magni-tools
THÖR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces
https://github.com/tphanson/xupr-drl
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
https://github.com/uditsharma29/flynet-cann
A Hybrid Compact Neural Architecture for Visual Place Recognition
https://github.com/umautobots/bidireaction-trajectory-prediction
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
https://github.com/unstablecursor/robotino_rl
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
https://github.com/ut-amrl/social_gym
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces
https://github.com/verbal-noun/a-star-pathfinding-family
Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions
https://github.com/verlab/3d-object-semantic-mapping
https://github.com/vkartik97/habitat-challenge-two-docker
https://github.com/vlmaps/vlmaps
https://github.com/vtolani95/HumANav-Release
Visual Navigation Among Humans with Optimal Control as a Supervisor
https://github.com/yangysc/resinet
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/yramon/ShapCounterfactual
Counterfactual Explanation Algorithms for Behavioral and Textual Data
https://github.com/yramon/edc
Counterfactual Explanation Algorithms for Behavioral and Textual Data
https://github.com/zhangwenyao1/open6dor_v2_execution
SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation
https://github.com/zubair-irshad/NeRF-MAE
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.