Awesome things about LLM-powered agents. Papers / Repos / Blogs / ...

This is a curated list of "Embodied AI or robot with Large Language Models" research. Watch this repository for the latest updates! 🔥

awesome grounding: A curated list of research papers in visual grounding

Autonomous Agents (LLMs) research papers. Updated Daily.

A curated list for vision-and-language navigation. ACL 2022 paper "Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions"

Democratization of RT-2 "RT-2: New model translates vision and language into action"

An open source framework for research in Embodied-AI from AI2.

A curated list of awesome papers on Embodied AI and related research/industry-driven resources.

Odyssey: Empowering Minecraft Agents with Open-World Skills

RAI is a multi-vendor agent framework for robotics, utilizing Langchain and ROS 2 tools to perform complex actions, defined scenarios, free interface execution, log summaries, voice interaction and more.

Seamlessly integrate state-of-the-art transformer models into robotics stacks

[arXiv 2023] Embodied Task Planning with Large Language Models

Official Repo of LangSuitE

[NIPSw 2024]This repo is the official implementation of "MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control "

[NeurIPS 2024] GenRL: Multimodal-foundation world models enable grounding language and video prompts into embodied domains, by turning them into sequences of latent world model states. Latent state sequences can be decoded using the decoder of the model, allowing visualization of the expected behavior, before training the agent to execute it.

Building open-ended embodied agent in battle royale FPS game

[ECCV 2024] STEVE in Minecraft is for See and Think: Embodied Agent in Virtual Environment

Official Implementation of NeurIPS'23 Paper "Cross-Episodic Curriculum for Transformer Agents"

A Production Tool for Embodied AI

Python code to implement LLM4Teach, a policy distillation approach for teaching reinforcement learning agents with Large Language Model