Foundation Models
SayCan
SayCan is a method from Google Research ("Do As I Can, Not As I Say", 2022) that grounds large language model planning in robot capabilities by combining the LLM's estimate of a skill's usefulness with a learned value function estimating the skill's probability of success in the current state. The system executed long-horizon kitchen tasks on mobile manipulators by iteratively selecting the skill with the highest combined score. It was an early and influential demonstration of LLMs as robot task planners.
Why it matters for physical AI
SayCan established the now-standard pattern of pairing internet-scale language reasoning with grounded affordance estimates, a lineage that runs directly to today's vision-language-action models.
Related terms
Build physical AI
Put these concepts to work on real hardware
Axol is a dual-arm robot built for physical AI — teleoperate it, collect demonstrations, and deploy learned policies out of the box.