Adapting Large Language Models for Generalist Embodied Navigation

Core Concepts

The authors propose NaviLLM, the first generalist model for embodied navigation, which adapts Large Language Models (LLMs) to unify a wide range of tasks through schema-based instruction.

Abstract

The authors present NaviLLM, the first generalist model for embodied navigation that adapts Large Language Models (LLMs) to handle diverse tasks. The key idea is to cast various embodied tasks into generation problems using schema-based instruction, which flexibly unifies tasks like vision-language navigation, object localization, 3D question answering, and trajectory summarization. The authors first introduce the scene encoding module that extracts multi-view representations from visual observations. Then, they design schema-based instruction, which consists of task, observation, history, and output hint, to transform different embodied tasks into generation problems that can be optimized using a unified cross-entropy objective. The authors train NaviLLM on a combined dataset covering various embodied tasks and evaluate its performance on multiple benchmarks. The results show that NaviLLM achieves new state-of-the-art on CVDN, SOON, and ScanQA, surpassing previous methods by a significant margin. Moreover, NaviLLM also demonstrates strong generalization capabilities, outperforming task-specific models on unseen tasks like embodied question answering. The authors attribute the success of NaviLLM to its ability to leverage the knowledge from pre-trained LLMs and unify diverse data sources through schema-based instruction.

Stats

The authors report the following key metrics: On CVDN, NaviLLM achieves a Goal Progress (GP) of 7.90, significantly outperforming the previous state-of-the-art method VLN-PETL which had a GP of 6.13. On SOON, NaviLLM achieves a Success Rate (SR) of 26.26% and a Success Rate weighted by Path Length (SPL) of 19.81%, surpassing the zero-shot DUET models by a large margin. On ScanQA, NaviLLM obtains an Exact Match (EM) of 26.3%, outperforming the previous state-of-the-art 3D-LLM by 7.2%.

Quotes

None.

Key Insights Distilled From

Towards Learning a Generalist Model for Embodied Navigation

by Duo Zheng,Sh... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2312.02010.pdf

Deeper Inquiries

How can the schema-based instruction be further extended to handle more complex task compositions, such as multi-step reasoning or hierarchical task structures?

The schema-based instruction approach can be extended to handle more complex task compositions by incorporating additional layers of abstraction and context. For multi-step reasoning tasks, the schema can be designed to include sequential steps, dependencies between steps, and decision points. Each step can have its own set of instructions, observations, and output hints, allowing the model to reason through a series of actions to achieve the final goal. Hierarchical task structures can be represented by nesting schemas within each other, with higher-level schemas providing overarching goals and lower-level schemas detailing subtasks. This hierarchical approach enables the model to break down complex tasks into manageable subtasks and execute them in a structured manner.

What are the potential limitations of the current approach, and how can it be improved to handle more diverse and challenging embodied navigation scenarios?

One potential limitation of the current approach is the reliance on pre-trained LLMs, which may not capture domain-specific nuances or intricacies required for certain embodied navigation tasks. To address this limitation, domain-specific fine-tuning or task-specific pre-training can be implemented to enhance the model's understanding of the specific task requirements. Additionally, the schema-based instruction may need further refinement to handle ambiguous or context-dependent instructions more effectively. Introducing dynamic schema generation or adaptive schema learning mechanisms can help the model adapt to diverse and challenging scenarios by adjusting the schema structures based on the task complexity and context.

Given the impressive generalization capabilities of NaviLLM, how can the insights from this work be applied to other domains beyond embodied navigation, such as robotic manipulation or multi-agent collaboration?

The insights from NaviLLM can be applied to other domains beyond embodied navigation by leveraging the generalization capabilities of the model and adapting the schema-based instruction approach to suit the requirements of different tasks. For robotic manipulation, the model can be trained on a diverse set of manipulation tasks with corresponding schemas for object interaction, tool usage, and spatial reasoning. By incorporating visual and textual inputs, the model can learn to perform complex manipulation tasks in real-world environments. In the context of multi-agent collaboration, the schema-based instruction can be extended to include communication protocols, coordination strategies, and shared goals among multiple agents. This approach enables the model to understand and execute collaborative tasks involving coordination, communication, and joint decision-making. By transferring the principles of schema-based instruction and multi-task learning, NaviLLM's insights can be effectively applied to a wide range of domains requiring intelligent interaction and decision-making.