itl-ed/llm-dp

LLM Dynamic Planner - Combining LLM with PDDL Planners to solve an embodied task

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Emerging

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.

No commits in the last 6 months.

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.

embodied-ai ai-agent-development task-planning simulated-environments robotics-research
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 12 / 25

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Stars

48

Forks

6

Language

Jupyter Notebook

License

MIT

Last pushed

Jan 04, 2025

Commits (30d)

0

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