Foundation Models

LLM Planning

LLM planning is the use of large language models to decompose natural-language goals into sequences of executable robot skills or code. SayCan grounded language model proposals with learned affordance values, Code as Policies generated executable policy code directly, and Inner Monologue incorporated textual feedback from the environment for closed-loop replanning. The LLM typically operates over a library of pretrained low-level skills rather than emitting motor commands itself.

Why it matters for physical AI

Language models contribute commonsense task knowledge that robot datasets lack, letting systems sequence long-horizon household or industrial tasks from a single instruction while learned skills handle the physical execution.