In-Context Learning Enables Robot Action Prediction in LLMs
This addresses the challenge of robot action prediction for robotics applications, representing an incremental advance by applying existing ICL methods to a new domain.
The paper tackles the problem of enabling text-only LLMs to predict robot actions without training by introducing RoboPrompt, a framework that uses in-context learning with keyframe-based textual descriptions, achieving stronger performance over baselines in simulated and real-world settings.
Recently, Large Language Models (LLMs) have achieved remarkable success using in-context learning (ICL) in the language domain. However, leveraging the ICL capabilities within LLMs to directly predict robot actions remains largely unexplored. In this paper, we introduce RoboPrompt, a framework that enables off-the-shelf text-only LLMs to directly predict robot actions through ICL without training. Our approach first heuristically identifies keyframes that capture important moments from an episode. Next, we extract end-effector actions from these keyframes as well as the estimated initial object poses, and both are converted into textual descriptions. Finally, we construct a structured template to form ICL demonstrations from these textual descriptions and a task instruction. This enables an LLM to directly predict robot actions at test time. Through extensive experiments and analysis, RoboPrompt shows stronger performance over zero-shot and ICL baselines in simulated and real-world settings. Our project page is available at https://davidyyd.github.io/roboprompt.