30 open-source projects similar to osu-nlp-group/llm-planner, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
ReAct is an agentic workflow template and prompting framework for large language models. It implements a logic pattern that integrates chain-of-thought reasoning with external tool execution to solve complex, multi-step tasks. The framework uses an interleaved reasoning and acting logic, forcing the model to document its internal thought process before executing an action. This cycle of planning and acting allows the system to interact with external APIs or databases and inject real-world data back into the model context to refine reasoning paths. The project covers autonomous task execution
Habitat-Lab is an open-source platform for training and evaluating embodied AI agents in photorealistic 3D indoor environments. It functions as a high-performance 3D indoor environment simulator that supports physics-based interaction, enabling research into navigation and manipulation tasks. The platform provides a modular task-environment abstraction that separates task logic from environment simulation, using configuration-driven pipeline assembly to compose simulation and training pipelines. It includes a hierarchical sensor-actuator architecture for mixing and matching perception and act
This repo contains the source code for making plans based on problems decribed by natural language.
An Embodied Generalist Agent in 3D World
Wenlong Huang 1 , Chen Wang 1 , Ruohan Zhang 1 , Yunzhu Li 1,2 , Jiajun Wu 1 , Li Fei-Fei 1
Official implementation of LLM+MAP: Bimanual Robot Task Planning using Large Language Models (LLMs) and Planning Domain Definition Language (PDDL). Codes and files are coming soon.
Voyager is an autonomous embodied agent and lifelong learning framework that uses a large language model to explore virtual environments. It functions as a code-based action controller, translating natural language instructions into executable scripts to interact with its surroundings. The system features an automatic curriculum generator that creates sequences of exploration goals to discover new items and behaviors without human intervention. It maintains a skill library manager that stores learned behaviors as reusable code fragments, which can be composed to execute complex tasks. The fr
[Paper](https://arxiv.org/abs/2210.05663) [Website](https://mahis.life/clip-fields/) [Code](https://github.com/notmahi/clip-fields) [Data](https://osf.io/famgv) [Video](https://youtu.be/bKu7GvRiSQU)
Unified Human-Scene Interaction via Prompted Chain-of-Contacts Zeqi Xiao   Tai Wang   Jingbo Wang   Jinkun Cao   Wenwei Zhang   Bo Dai   Dahua Lin   Jiangmiao Pang*   Shanghai AI Laboratory Nanyang Technological University Carnegie Mellon University
STEVE, named after the protagonist of the game Minecraft, is our proposed framework aims to build an embodied agent based on the vision model and LLMs within an open world.
FILM: Following Instructions in Language with Modular Methods So Yeon Min, Devendra Singh Chaplot, Pradeep Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov Carnegie Mellon University, Facebook AI Research