I3L: Why Corrections Beat More Demos in Mobile Manipulation
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…
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