Topic

Embodied memory for robot policies

Latest viewpoints

MAM system combines short-term visual and long-term textual memory for robots.

Marcel presents MAM (multi-scale embodied memory), a system that decomposes memory into short-term visual memory for low-level dexterous control and long-term textual memory for high-level policy planning, enabling robots to track task progress and adapt to mistakes during long-horizon tasks.

Marcel Torne Villasevil Embodied memory for robot policies Source at 7:59

Leaders

Jensen Huang

CEO, Nvidia

Last interview Sep 2, 2026

Howard Lutnick

Public business viewpoint profile

Last interview Sep 2, 2026

Sam Altman

CEO, OpenAI

Last interview Sep 2, 2026

Elon Musk

CEO, SpaceX / Tesla

Last interview Sep 1, 2026

Julien Bek

Partner, Sequoia Capital

Last interview Aug 24, 2026

Thibault Sottiaux

Engineer, OpenAI

Last interview Aug 24, 2026

Matthew Berman

Influencer, Forward Future

Last interview Aug 24, 2026

David Senra

Podcaster, Senra Show

Last interview Aug 23, 2026

LeAnn Foster

Founder, EUTHENICS

Last interview Aug 22, 2026

Eric Ho

CEO, Goodfire

Last interview Aug 21, 2026