Papers with Code 아카이브
Robot Navigation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/sled-group/navchat
공식 코드Think, Act, and Ask: Open-World Interactive Personalized Robot Navigation
https://github.com/slothfulxtx/TransLoc3D
공식 코드TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields
https://github.com/snlemons/search
공식 코드https://github.com/solislemuslab/phylo-diamond.jl
공식 코드https://github.com/solislemuslab/phylodiamond.jl
공식 코드https://github.com/speckdavid/rl-plan
공식 코드Learning Heuristic Selection with Dynamic Algorithm Configuration
https://github.com/srama2512/PONI
공식 코드PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning
https://github.com/tarekshohdy/ppo_sofa_soft_legged_robot
공식 코드Development of a PPO-Reinforcement Learned Walking Tripedal Soft-Legged Robot using SOFA
https://github.com/tessavdheiden/SCR
공식 코드Social Navigation with Human Empowerment driven Deep Reinforcement Learning
https://github.com/tfburns/State-Complexes-of-Gridworlds
공식 코드Detecting danger in gridworlds using Gromov's Link Condition
https://github.com/thomasdengiz/multiobjective_localsearch_dsm
공식 코드https://github.com/tianchenji/paad
공식 코드Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion
https://github.com/tmralmeida/thor-magni-actions
공식 코드THÖR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces
https://github.com/topel/SGLThresh
공식 코드Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning
https://github.com/tri-ml/haicu
공식 코드Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty
https://github.com/umich-curly/fetch_irl
공식 코드https://github.com/ut-amrl/graph_navigation
공식 코드Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization
https://github.com/vanderschaarlab/deepgenerativesymbolicregression
공식 코드https://github.com/vdorbala/LGX
공식 코드https://github.com/vijay4313/proxemo
공식 코드https://github.com/vita-epfl/CrowdNav
공식 코드https://github.com/vita-epfl/DyNav
공식 코드https://github.com/vvanirudh/CMAXPP
공식 코드CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models
https://github.com/wang-chen/correlation_flow
공식 코드Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
https://github.com/weijia-yao/cdc20_singularity
공식 코드Vector Field Guided Path Following Control: Singularity Elimination and Global Convergence
https://github.com/weiqimeng1/pointcloud_skeletonization_metrics
공식 코드Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics
https://github.com/wh200720041/SRLCD
공식 코드Online Visual Place Recognition via Saliency Re-identification
https://github.com/wqi/A2L
공식 코드https://github.com/wsakobe/cylindertag
공식 코드https://github.com/xarakas/spheroid-tnf-v2-emews
공식 코드https://github.com/xbrlej/fipomdp
공식 코드https://github.com/xiaolisean/milpsafetyguard
공식 코드System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models
https://github.com/xinjinghao/color
공식 코드https://github.com/xxlong0/CNMNet
공식 코드Occlusion-Aware Depth Estimation with Adaptive Normal Constraints
https://github.com/xxlong0/ESTDepth
공식 코드Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.