Episode Details

Long-horizon robotic manipulation requires a policy to bridge task-level semantic reasoning with metric three-dimensional interaction geometry. Existing vision–language–action policies usually acquire geometry implicitly from visual tokens or introduce deterministic intermediate representations that lack spatial grounding. This paper proposes SIRModel, which learns a spatial intermediate representation to parameter-efficiently fine-tune a vision language model for robotic manipulation tasks. In this episode of Embodied AI 101, we explore "SIRModel and the Missing Geometry Layer in Vision–Language Manipulation". 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.