https://github.com/kaist-avelab/k-lane
공식 코드K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways
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/kevin-thu/epona
공식 코드Epona: Autoregressive Diffusion World Model for Autonomous Driving
https://github.com/lab176344/TrafficScenarios-RFAPsClustering
공식 코드https://github.com/lancial/orrn_public
공식 코드https://github.com/lasmp23/lasmp
공식 코드https://github.com/lasuomela/carla_vloc_benchmark
공식 코드https://github.com/layer6ai-labs/CAE
공식 코드C-Learning: Horizon-Aware Cumulative Accessibility Estimation
https://github.com/lcalem/diva
공식 코드Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints
https://github.com/linfunster/pp-til
공식 코드PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning
https://github.com/liruiw/GA-DDPG
공식 코드Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
https://github.com/luke-ck/vgpmp
공식 코드A Unifying Variational Framework for Gaussian Process Motion Planning
https://github.com/maximecb/gym-miniworld
공식 코드Towards real-world navigation with deep differentiable planners
https://github.com/mees/vapo
공식 코드Affordance Learning from Play for Sample-Efficient Policy Learning
https://github.com/mehradmrt/ucm-agbot-ros2
공식 코드https://github.com/memberre/asyncdriver
공식 코드Asynchronous Large Language Model Enhanced Planner for Autonomous Driving
https://github.com/mfe7/cadrl_ros
공식 코드Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
https://github.com/mh0797/interplan
공식 코드Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
https://github.com/mingyucai/E-LDGBA_RL
공식 코드Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfaction
https://github.com/mingyucai/Modular_Deep_RL_E-LDGBA
공식 코드Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic
https://github.com/mit-acl/faster
공식 코드FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments
https://github.com/mjysh/RL3linkFish
공식 코드https://github.com/msuv08/autonomous-vehicle-drifting
공식 코드Learning Inverse Kinodynamics for Autonomous Vehicle Drifting
https://github.com/noemiejaquier/materngabo
공식 코드Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
https://github.com/nv-tlabs/lift-splat-shoot
공식 코드Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D
https://github.com/nvlabs/motion-policy-networks
공식 코드https://github.com/nvr-avg/scept
공식 코드ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning
https://github.com/omron-sinicx/neural-astar
공식 코드[Reproducibility Report] Path Planning using Neural A* Search
https://github.com/opendrivelab/occnet
공식 코드https://github.com/opengvlab/drivemlm
공식 코드https://github.com/owensun2004/Manual2Skill
공식 코드https://github.com/pkicki/cnp-b
공식 코드Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
https://github.com/pointscoder/gpt-driver
공식 코드https://github.com/proroklab/private_flocking
공식 코드An Adversarial Approach to Private Flocking in Mobile Robot Teams
https://github.com/qualcomm-ai-research/geometric-algebra-transformer
공식 코드https://github.com/rafaelpossas/bayes_sim
공식 코드BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators
https://github.com/rainorangelemon/gnn-motion-planning
공식 코드Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.