Foundation Models

In-Context Learning

In-context learning is the ability of large pretrained models to adapt to new tasks from examples or instructions placed in their input prompt, without any weight updates. In robotics, the paradigm appears as policies conditioned on a handful of demonstration trajectories at inference time, as in one-shot imitation learning, and as LLM planners steered by few-shot examples of task decompositions.

Why it matters for physical AI

Adapting a deployed robot by showing it a demonstration, rather than launching a fine-tuning run, would collapse the cost of teaching new tasks from days to minutes.