itl-ed/llm-dp
LLM Dynamic Planner - Combining LLM with PDDL Planners to solve an embodied task
This project helps AI researchers and developers working with embodied AI agents to dynamically generate plans for complex, multi-step tasks in simulated environments. It takes a task description and a simulated environment's state, then outputs a sequence of actions for an agent to follow. This is designed for those building and testing AI agents that need to reason and act sequentially to achieve goals.
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Use this if you are developing or evaluating AI agents in a simulated world and need them to autonomously plan and execute long-horizon tasks.
Not ideal if you are looking for a plug-and-play solution for real-world robotics or if your tasks are simple and don't require complex sequential reasoning.
Stars
48
Forks
6
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 04, 2025
Commits (30d)
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