https://github.com/smarton-empower/smarton-ai
공식 코드New Interaction Paradigm for Complex EDA Software Leveraging GPT
Papers with Code 아카이브
Task Planning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/snumprlab/flare
공식 코드https://github.com/taskography/taskography
공식 코드TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs
https://github.com/tri-ml/prismatic-vlms
공식 코드Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/tri-ml/vlm-evaluation
공식 코드Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/ttianyuren/eTAMP
공식 코드https://github.com/usc-gvl/agent-driver
공식 코드https://github.com/visual-action-planning/lsr-v2-code
공식 코드Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
https://github.com/vlc-robot/robot-3dlotus
공식 코드Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy
https://github.com/waynemao/robomatrix
공식 코드https://github.com/wentaoyuan/sornet
공식 코드SORNet: Spatial Object-Centric Representations for Sequential Manipulation
https://github.com/wonderful-team-robotics/wonderful_team_robotics
공식 코드Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs
https://github.com/wxxshirley/gnn4taskplan
공식 코드https://github.com/x-plug/multi-llm-agent
공식 코드https://github.com/xfgao/VRKitchen
공식 코드VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning
https://github.com/xiao-wen-sun/ossa
공식 코드Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning
https://github.com/xwinks/llm_articulated_object_manipulation
공식 코드Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs
https://github.com/yan98/gta1
공식 코드https://github.com/yding25/GPT-Planner
공식 코드Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
https://github.com/zcaicaros/pmc
공식 코드Planning with Multi-Constraints via Collaborative Language Agents
https://github.com/zhehuazhou/isr-llm
공식 코드ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
https://github.com/zhourobotics/llm-mrs-survey
공식 코드https://github.com/zihao-ai/eairiskbench
공식 코드https://github.com/zihao-ai/earbench
공식 코드https://github.com/zsq259/plan-over-graph
공식 코드https://github.com/zyjiao4728/POG-Demo
공식 코드https://github.com/EmaroLab/industrialRobot_task_planning
https://github.com/MindCode-4/code-12/tree/main/isr
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
https://github.com/RylanSchaeffer/prismatic-vlms
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/ais-bonn/nimbro_api
A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
https://github.com/bravegroup/sheetcopilot
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
https://github.com/craftjarvis/rat
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
https://github.com/davidpaulius/foon_api
Task Planning with a Weighted Functional Object-Oriented Network
https://github.com/jhu-lcsr/costar_plan
https://github.com/microsoft/recai
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
https://github.com/princeton-nlp/swe-agent
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.