Prompt a Robot to Walk with Large Language Models (2309.09969v3)

Abstract: LLMs pre-trained on vast internet-scale data have showcased remarkable capabilities across diverse domains. Recently, there has been escalating interest in deploying LLMs for robotics, aiming to harness the power of foundation models in real-world settings. However, this approach faces significant challenges, particularly in grounding these models in the physical world and in generating dynamic robot motions. To address these issues, we introduce a novel paradigm in which we use few-shot prompts collected from the physical environment, enabling the LLM to autoregressively generate low-level control commands for robots without task-specific fine-tuning. Experiments across various robots and environments validate that our method can effectively prompt a robot to walk. We thus illustrate how LLMs can proficiently function as low-level feedback controllers for dynamic motion control even in high-dimensional robotic systems. The project website and source code can be found at: https://prompt2walk.github.io/ .

Summary of "Prompt a Robot to Walk with LLMs"

The paper "Prompt a Robot to Walk with LLMs" introduces a novel approach to leveraging LLMs, such as GPT-4, for controlling robots, specifically to prompt a quadruped robot to walk. The authors propose a paradigm shift in robotics control from pre-trained and fine-tuned models to a few-shot learning approach using text prompts. This research demonstrates that LLMs, without fine-tuning on task-specific data, can effectively generate dynamic robot motion commands and function as low-level feedback controllers.

Methodology

The authors outline a framework where dynamics data, captured in a few-shot prompting mechanism, serve as textual interface between the robot's physical interactions and the LLM. There are two main components of the text prompt:

1. Description Prompt: This includes structured textual information on the task requirements, including descriptions of the input and output space, joint configurations, the complete control pipeline, and additional illustrations.
2. Observation and Action Prompt: This comprises a series of historical observations and actions recorded during the robot's interactions with its environment. The data is converted to a text-based input format and is normalized to enhance understanding by LLMs, which are trained primarily on text data.

The proposed LLM-based control does not require task-specific fine-tuning, but instead uses prompts engineered to interface with GPT-4. This approach enables real-time inference on target joint positions, which is integral to the robot's walking mechanics. The system operates at a simulated frequency of 10 Hz, which is suitable for prompt generation and dynamic motion execution.

Key Results

The paper validated the proposed framework on two robotic platforms: the A1 quadruped and the ANYmal robot, under various terrains and simulators. The experimental results showed that the LLM output trajectories significantly diverged from those produced by RL policies, suggesting that LLMs might be learning gait patterns distinct from established behavior-cloning approaches. These outcomes imply that LLMs can serve as effective low-level controllers by generating dynamic walking motions based purely on in-context learning from few-shot prompts.

Analysis of Prompt Design

The effectiveness of the LLM in controlling robot walking was highly contingent on the comprehensiveness and clarity of the text prompts. Experiments demonstrated that a well-crafted description prompt integrated with a sequence of contextualized observation-action pairs could significantly enhance both the normalized walking time and the success rate of the robot's walking tasks. Further analysis revealed that normalizing numerical data to integer values played a crucial role in enhancing the LLM's ability to interpret and act upon the given prompts.

Discussion and Future Directions

This research highlights a critical advancement in utilizing LLMs for robotics. However, it presents some limitations, such as the requirement for structured prompt engineering and the speed of inferences, primarily due to the constraints of the current LLM's computational efficiency. Future research could explore hardware optimization, extending this framework to other dynamic tasks, and refining prompt formats for greater abstraction and efficiency. A deeper understanding of the LLMs' in-context learning mechanisms could enhance their application in complex, real-world robotic systems.

This paper exemplifies a pioneering step towards integrating decision-making and control functionalities directly with LLMs, potentially altering the landscape of robotic control systems by utilizing foundational models trained on textual data. As LLMs evolve, their applicability across more nuanced and intricate robotic tasks might be expanded, enabling even more refined control capabilities.