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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 learning principle from the whole-body systems contribution. The central limitation is experimental: the comparison isolating iterative collection from a single correction batch is performed in simulation, not on the physical household tasks. For researchers building human-in-the-loop training systems, the episode focuses on choosing useful supervision and turning interventions into autonomous behavior. When your robot repeatedly misses the same grasp, should your next dataset contain another perfect demonstration—or the recovery it actually needs?
