GAM: Ground the Target Before You Learn the Action
from Embodied AI 101 Hosted by Shaoqing Tan
GAM is a robot foundation model that conditions action chunk prediction on 3D grounded inputs — including language, 3D point clouds, and bounding boxes — alongside robot history, achieving strong results on LIBERO benchmarks and real-robot tasks. The 3D grounding mechanism advances how embodied agents spatially understand and act in their environment. In this episode of Embodied AI 101, we explore "GAM: Ground the Target Before You Learn the Action". We break down the research, methodology, and real-world implications for robotics, AI, and physical intelligence. Embodied AI 101 covers the latest research at the intersection of AI and physical intelligence — robotics, manipulation, world models, and the path from digital intelligence to embodied agents.
Embed this episode
What this episode covers
GAM is a robot foundation model that conditions action chunk prediction on 3D grounded inputs — including language, 3D point clouds, and bounding boxes — alongside robot history, achieving strong results on LIBERO benchmarks and real-robot tasks. The 3D grounding mechanism advances how embodied agents spatially understand and act in their environment.
No transcript yet.