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/rst-tu-dortmund/lord
Parting with Misconceptions about Learning-based Vehicle Motion Planning
https://github.com/simonzhan-code/step-wise_saferl_pixel
https://github.com/sled-group/driVLMe
https://github.com/stevezhang1990/dma_rl
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
https://github.com/sueda/redmax
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact
https://github.com/tomsilver/camps
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
https://github.com/tomsilver/ploi
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
https://github.com/ucsdarclab/motion_planning_transformer
Motion Planning Transformers: A Motion Planning Framework for Mobile Robots
https://github.com/umass-embodied-agi/coela
https://github.com/umass-foundation-model/co-llm-agents
https://github.com/vaibhavsaxena11/cwvae
https://github.com/walzimmer/bat-3d
https://github.com/wanxinjin/Pontryagin-Differentiable-Programming
https://github.com/wenkaip-1836890/pyromid_l5prediction
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/westny/dronalize
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
https://github.com/wuziyi616/multi_part_assembly
RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assembly
https://github.com/wwooo/tensorflow_complex_yolo
https://github.com/xingyu-lin/softagent
https://github.com/yijiangh/coop_assembly
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
https://github.com/yongchao98/multi-agent-framework
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
https://github.com/yongchao98/nl2tl
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
https://github.com/zhouxian/FluidLab
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.