December 2023
tl;dr: A multi-LLM pipeline for active exploration of new environments.
LLM-brain is a multi-LLM pipline that acts as a robotic brain to unify ecocentric memory and control. The multiple agents communicate by natural language, provideing excellenet explanability.
Active exploration: extensively explore the unknown enviromenmt within a limited number of actions.
Reinforcement learning have dominated the field of embodied AI for many years until LLM in the field of Internet AI emerges.
The results of LLM-brain is not that impressive, but it provides a reasonable starting framework for active exploration.
- Three agents
- Eye: VQA model (BLIP-2), takes in images and questions.
- Nerve: ask questions to Eye, and provide summary to Brain
- Why do we need nerve? We need to summarize the action and environment. "I move forward, and I see a sofa". This is inspired by ChatCaptioner.
- Brain: takes in high level human instructions, memorize previous action and (potentially partial) environment observations, and infers next action based on its world knowledge.
- Embodied AI tasks
- Embodied QA (VQA)
- Active exploration: "explore the house as completely as possible", evaluted by the explored areas vs total area.
- LM-nav: vision-language navigation
- PointNav task
- Dataset: matterport3D.
- On the ask of active exploration, LLM-brain provides a slightly better performance than the random baseline.
- Questions and notes on how to improve/revise the current work