I3L: Why Corrections Beat More Demos in Mobile Manipulation

Podcast: Embodied AI 101 · Shaoqing Tan

Opublikowano: 2026-10-31

Can a mobile manipulation policy become dependable by learning from the moments when a human takes over? This episode explains how I3L combines whole-body correction with iterative imitation learning, and what its experiments establish. We examine three questions: how JoyLo+ transfers control of the arms, torso, and base; why corrective experience can outperform additional demonstrations; and what updating the policy between collection rounds contributes. The evaluation combines controlled simulation, contact-rich Franka manipulation, and real Galaxea R1 Pro household tasks, using diffusion policies and a pi-zero-point-five vision-language-action model. We place the approach alongside HG-DAgger, RoboCopilot, and SIRIUS to distinguish the...