Papers with Code 아카이브
Motion Planning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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<span style="color:grey; opacity: 0.6">( Image credit: Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://gitee.com/gai-shaoyan/mind3d
https://github.com/AI-liu/Complex-YOLO
https://github.com/Ankur-Deka/Hiding-Leader-Identity
An Adversarial Approach to Private Flocking in Mobile Robot Teams
https://github.com/CeroChen/GraphBashedCoordination
https://github.com/CognitiveAISystems/Dynamic-Neural-Potential-Field
Neural Potential Field for Obstacle-Aware Local Motion Planning
https://github.com/DataScienceNigeria/Joint-Language-to-Pose-JL2P-with-3D-pose
https://github.com/Jianghanxiao/PhysTwin
PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable Objects from Videos
https://github.com/JrkJamie/eagermot_ms
https://github.com/Kaixhin/PlaNet
https://github.com/LeeLinJun/mpc-mpnet-py
https://github.com/MindCode-4/code-8/tree/main/multimodal-trajectory
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/MoonBlvd/tad-IROS2019
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
https://github.com/MoonBlvd/tad-IROS2019-TBD-
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov3
https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov4
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov3
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov4
https://github.com/RogerQi/CVAE_Motion_Planning
https://github.com/SHITIANYU-hue/Efficient-motion-planning
https://github.com/Yizhao111/dreamer-pytorch
https://github.com/abefetterman/hamstir-gym
CAD2RL: Real Single-Image Flight without a Single Real Image
https://github.com/ahq1993/MPNet
https://github.com/alin256/multi-mode-prediction-with-mtp-loss
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/antheali/3dpartassembly
https://github.com/aorthey/MotionExplorer
Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree
https://github.com/astrohiro/ancm
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
https://github.com/astrohiro/cvstem
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
https://github.com/astrohiro/nscm
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
https://github.com/caelan/SS-Replan
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
https://github.com/cbschaff/benchmark-rrc
Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge
https://github.com/chandar-lab/LoCA2
https://github.com/cmc623/Formation-control-with-lane-preference
https://github.com/cmc623/flexible-lane-direction-intersection
https://github.com/cmc623/formation-control-experiments
https://github.com/columbia-robovision/decentralized-multiarm
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.