https://github.com/huangwl18/language-planner
공식 코드Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
APEX: Empowering LLMs with Physics-Based Task Planning for Real-time Insight
Graphormer-Guided Task Planning: Beyond Static Rules with LLM Safety Perception
RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
Getting pwn'd by AI: Penetration Testing with Large Language Models
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents
BEDI: A Comprehensive Benchmark for Evaluating Embodied Agents on UAVs
NNgTL: Neural Network Guided Optimal Temporal Logic Task Planning for Mobile Robots
SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models
LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models
Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution
WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models
Vinci: A Real-time Embodied Smart Assistant based on Egocentric Vision-Language Model
Robotouille: An Asynchronous Planning Benchmark for LLM Agents
Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs
REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.