Foundation Model

Foundation models are neural networks trained on massive datasets (text, images, video, sensor data) to learn general-purpose representations. Large language models (LLMs) like GPT learn from internet-scale text data. Vision transformers learn from millions of images. Multimodal models combine multiple data types. These models develop broad capabilities that transfer to new tasks with minimal additional training (fine-tuning). In robotics, foundation models provide high-level reasoning, language understanding, and common-sense knowledge. The robot uses the foundation model as a cognitive core, querying it for task planning, object recognition, natural language interaction, and decision-making while specialized systems handle low-level control.

Foundation models enable humanoid robots to understand natural language commands and engage in conversations. Vision-language models help robots identify objects and understand spatial relationships. Task planning uses foundation models to decompose complex goals into executable steps. Code generation models help robots write control scripts from descriptions. Common-sense reasoning models help robots make appropriate decisions in novel situations. Multimodal models integrate vision, language, and action for unified understanding. Few-shot learning allows robots to learn new tasks from minimal examples.