LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
This work addresses the challenge of developing versatile and sample-efficient embodied agents for complex tasks in visually-perceived environments, representing an incremental improvement over existing methods.
The study tackled the problem of high data cost and poor sample efficiency in planning for embodied agents by proposing LLM-Planner, a method using large language models for few-shot grounded planning, achieving competitive performance on the ALFRED dataset with less than 0.5% of training data.
This study focuses on using large language models (LLMs) as a planner for embodied agents that can follow natural language instructions to complete complex tasks in a visually-perceived environment. The high data cost and poor sample efficiency of existing methods hinders the development of versatile agents that are capable of many tasks and can learn new tasks quickly. In this work, we propose a novel method, LLM-Planner, that harnesses the power of large language models to do few-shot planning for embodied agents. We further propose a simple but effective way to enhance LLMs with physical grounding to generate and update plans that are grounded in the current environment. Experiments on the ALFRED dataset show that our method can achieve very competitive few-shot performance: Despite using less than 0.5% of paired training data, LLM-Planner achieves competitive performance with recent baselines that are trained using the full training data. Existing methods can barely complete any task successfully under the same few-shot setting. Our work opens the door for developing versatile and sample-efficient embodied agents that can quickly learn many tasks. Website: https://dki-lab.github.io/LLM-Planner