GOMA: Proactive Embodied Cooperative Communication Framework

Core Concepts

GOMA proposes a novel cooperative communication framework for embodied assistants to proactively communicate with humans, optimizing cooperation by aligning mental states.

Abstract

In this paper, the authors introduce GOMA, a proactive embodied cooperative communication framework. The content is structured as follows:

Introduction to the importance of verbal communication in human cooperation.
Proposal of Goal-Oriented Mental Alignment (GOMA) framework.
Evaluation of GOMA against baselines in Overcooked and VirtualHome environments.
Discussion on joint planning, theory of mind, and problem formulation.
Detailed experiments and results in Overcooked and VirtualHome simulations.
Human experiment setup and results with subjective ratings.
Conclusion highlighting the effectiveness of GOMA in achieving optimal cooperation.

Stats

Verbal communication plays a crucial role in human cooperation.
Large language models struggle with generating meaningful communication grounded in context.
Experimental results show GOMA outperforms strong baselines in challenging environments.

Quotes

"Verbal communication serves to align the mental states of agents."
"Our experimental results demonstrate that large language models struggle with generating meaningful communication."

Key Insights Distilled From

GOMA

by Lance Ying,K... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11075.pdf

Deeper Inquiries

How can GOMA be applied to real-world scenarios beyond simulations

GOMA can be applied to real-world scenarios beyond simulations by enhancing the communication and collaboration between humans and AI assistants in various settings. For instance, in customer service, GOMA can help AI chatbots better understand user needs and provide more relevant assistance. In healthcare, GOMA can improve coordination between medical professionals and AI systems for accurate diagnosis and treatment planning. Additionally, in smart home environments, GOMA can facilitate seamless interaction between users and intelligent devices for efficient task completion.

What are potential drawbacks or limitations of using GOMA for embodied AI assistants

Despite its effectiveness, there are potential drawbacks or limitations of using GOMA for embodied AI assistants. One limitation is the complexity of modeling human mental states accurately, as human behavior is nuanced and context-dependent. This could lead to misinterpretations or misunderstandings during communication exchanges. Another drawback is the reliance on natural language processing models like LLMs, which may struggle with generating coherent communication grounded in social context or physical environment details consistently.

How can understanding theory of mind improve human-AI collaboration

Understanding theory of mind can significantly improve human-AI collaboration by enabling AI systems to infer human intentions, beliefs, desires, and emotions accurately. By incorporating theory of mind reasoning into AI algorithms like GOMA, machines can anticipate human actions better and adapt their responses accordingly. This leads to more effective communication strategies that align with human expectations and preferences. Ultimately, a deeper understanding of theory of mind enhances empathy in AI interactions and fosters smoother collaboration between humans and intelligent agents across various domains.