This paper examines strategies for integrating foundation models in robotic systems, revealing trade-offs in instruction grounding and action efficiency.
Key Points
Performance limits expose trade-offs in generalization and data efficiency during robotic task execution.
Two focused case studies highlight distinct paradigms: vision-language-action models and modular pipelines.
Fine-grained instruction understanding is assessed through complex instruction grounding tasks.
Robotics integration of foundation models presents emerging challenges and opportunities in real-world applications.