Papers with Code 아카이브
Motion Planning
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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://github.com/dC4rlos/TFM
https://github.com/daeheepark/PathPredictNusc
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/georgeliu233/scene-rep-transformer
Learning hierarchical behavior and motion planning for autonomous driving
https://github.com/geyang/plan2vec
Plan2Vec: Unsupervised Representation Learning by Latent Plans
https://github.com/ghimiredhikura/Complex-YOLO-V3
https://github.com/ghimiredhikura/Complex-YOLOv3
https://github.com/gnns4hri/socnavgym
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
https://github.com/govvijaycal/confidence_aware_predictions
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
https://github.com/heidelberg-hepml/lorentz-gatr
https://github.com/idsc-frazzoli/dg-commons
https://github.com/ingyn/macsrl
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning
https://github.com/jingxixu/Kitchen2D---stir
Active model learning and diverse action sampling for task and motion planning
https://github.com/jingxixu/pddlstream
https://github.com/juliuskunze/cwvae-jax
https://github.com/kazuki-shin/l5kit
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/kumgleb/Lyft_motion_prediction
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/maudzung/Complex-YOLOv4-Pytorch
https://github.com/miaoruonan/MACA_test
Socially Aware Motion Planning with Deep Reinforcement Learning
https://github.com/mit-acl/cadrl_ros
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
https://github.com/mit-acl/gym-collision-avoidance
Socially Aware Motion Planning with Deep Reinforcement Learning
https://github.com/mit-acl/mppi_numba
Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map
https://github.com/mit-acl/rl_collision_avoidance
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
https://github.com/motional/nuplan-devkit
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
https://github.com/mstoelzle/solving-occlusion
Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning
https://github.com/nanzhaogang/contrib/tree/master/application/multimodal-trajectory-predictions-for-autonomous-driving-using-deep-convolutional-networks
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/nishadg246/stripstream-ivan-nishad
https://github.com/omron-sinicx/planning-datasets
https://github.com/opendrivelab/openscene
https://github.com/our-projects-github/Safe-Deep-Learning-Based-Global-Path-Planning-Using-a-Fast-Collision-Free-Path-Generator
Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation
https://github.com/pku-epic/unidexgrasp
https://github.com/proroklab/vectorizedmultiagentparticlesimulator
https://github.com/proroklab/vectorizedmultiagentsimulator
https://github.com/pwc-1/Paper-5/tree/main/mpt
MPTP: Motion-Planning-aware Task Planning for Navigation in Belief Space
https://github.com/r-pad/zephyr
https://github.com/ripl-ttic/real-robot-challenge
Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.