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/tum-avs/occlusionawaremotionplanning
공식 코드From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving
https://github.com/tvpian/project-tar
공식 코드https://github.com/um-iral/resource-aware-coordination-airsim
공식 코드Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks
https://github.com/uml-robotics/projection_mapping
공식 코드https://github.com/uoe-agents/cema
공식 코드Causal Explanations for Sequential Decision-Making in Multi-Agent Systems
https://github.com/usc-gvl/agent-driver
공식 코드https://github.com/vdorbala/LGX
공식 코드https://github.com/vishal-2000/EDMP
공식 코드EDMP: Ensemble-of-costs-guided Diffusion for Motion Planning
https://github.com/vlc-robot/robot-3dlotus
공식 코드Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy
https://github.com/walzimmer/3d-bat
공식 코드https://github.com/wanxinjin/Learning-from-Directional-Corrections
공식 코드https://github.com/wanxinjin/Learning-from-Sparse-Demonstrations
공식 코드https://github.com/wanxinjin/Safe-PDP
공식 코드https://github.com/wayveai/mile
공식 코드https://github.com/wenbowen123/iros20-6d-pose-tracking
공식 코드Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks
https://github.com/westny/mtp-go
공식 코드MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
https://github.com/wkentaro/reorientbot
공식 코드ReorientBot: Learning Object Reorientation for Specific-Posed Placement
https://github.com/wkentaro/safepicking
공식 코드SafePicking: Learning Safe Object Extraction via Object-Level Mapping
https://github.com/wuxiyang1996/iPLAN
공식 코드https://github.com/wzm5853/bench4merge
공식 코드https://github.com/wzzheng/doe
공식 코드Doe-1: Closed-Loop Autonomous Driving with Large World Model
https://github.com/wzzheng/gaussianad
공식 코드https://github.com/wzzheng/gpd
공식 코드https://github.com/xheon/panoptic-reconstruction
공식 코드https://github.com/y1kee/cvf-for-nonholonomic-motion-planning
공식 코드Curvature-Constrained Vector Field for Motion Planning of Nonholonomic Robots
https://github.com/yongchao98/autotamp
공식 코드AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
https://github.com/yoraish/mmd
공식 코드https://github.com/yotakayama/stlccp
공식 코드STLCCP: Efficient Convex Optimization-based Framework for Signal Temporal Logic Specifications
https://github.com/yuangao-tum/riskaware-scenario-analyse
공식 코드https://github.com/zhang-zengjie/code_2023_iecon_shaping_wu
공식 코드Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
https://github.com/zhang-zengjie/ram-ruts
공식 코드Risk-Aware MPC for Stochastic Systems with Runtime Temporal Logics
https://github.com/zhourobotics/llm-mrs-survey
공식 코드https://github.com/zi-w/Kitchen2D
공식 코드Active model learning and diverse action sampling for task and motion planning
https://github.com/zlatanajanovic/SBMP_PerfDriving
공식 코드Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles
https://github.com/zlichen/ppad
공식 코드PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.