Can LLMs Generate Human-Like Wayfinding Instructions? Platform-Agnostic Embodied Instruction Synthesis

Centrala begrepp

LLMs can generate human-like wayfinding instructions in a platform-agnostic manner, revolutionizing embodied navigation tasks.

Sammanfattning

In this study, a novel approach is presented to automatically synthesize wayfinding instructions for embodied robot agents. The method uses in-context learning with LLMs to generate instructions across multiple simulation platforms. A user study showed that 83.3% of users found the synthesized instructions accurately captured environment details. Zero-shot navigation experiments demonstrated comparable performance with human-annotated data. The generated instructions were found to be usable and scalable for creating wayfinding instructions.

Statistik

83.3% of users find the synthesized instructions accurately capture the details of the environment.
< 1% change in SR observed in zero-shot navigation experiments.

Citat

"83.3% of users believe that the generated instruction captured details of the environment accurately."
"Generated instructions can serve as a good replacement for human-annotated data."

Viktiga insikter från

Can LLMs Generate Human-Like Wayfinding Instructions? Towards Platform-Agnostic Embodied Instruction Synthesis

by Vishnu Sasha... på arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11487.pdf

Djupare frågor

How can the platform-agnostic nature of instruction synthesis impact future research in robotics?

The platform-agnostic nature of instruction synthesis can have a significant impact on future research in robotics by promoting cross-platform generalizability and scalability. Researchers will no longer be limited to specific simulation environments when generating instructions for embodied agents, allowing for more versatile and adaptable robotic systems. This flexibility enables researchers to conduct experiments across different simulators without the need for simulator-specific human-annotated data, ultimately enhancing the efficiency and effectiveness of robotic navigation algorithms. Additionally, the ability to generate instructions that are compatible with various platforms fosters collaboration and knowledge sharing within the robotics community, leading to advancements in robot learning and autonomy.

What potential challenges could arise from relying solely on LLM-generated instructions for embodied navigation?

While leveraging LLMs for instruction generation offers numerous benefits, there are potential challenges that may arise when relying solely on these models for embodied navigation tasks. One key challenge is ensuring the accuracy and reliability of generated instructions, as LLMs may occasionally produce incorrect or misleading guidance due to limitations in understanding context or complex spatial relationships. Moreover, there might be instances where LLM-generated instructions lack nuanced details or fail to capture subtle environmental cues essential for successful navigation. Another challenge is the interpretability of LLM-generated instructions, as they may not always provide transparent reasoning behind their decision-making process, making it difficult to troubleshoot errors or refine performance based on feedback.

How might advancements in language models like LLMs influence human-robot collaboration beyond navigation tasks?

Advancements in language models like LLMs have the potential to revolutionize human-robot collaboration beyond navigation tasks by enabling more natural and intuitive interactions between humans and robots. These advanced language models can facilitate seamless communication between humans and robots through spoken or written dialogue, enhancing task delegation, information exchange, and overall teamwork efficiency. Furthermore, sophisticated language capabilities empower robots to understand complex commands, respond appropriately to inquiries or requests from humans, and adapt their behavior based on real-time feedback. As a result, human-robot collaboration becomes more interactive, adaptive, and productive across various domains such as healthcare assistance, customer service support,
and educational settings.