Mulligan: Can Failure-Focused Data Improve Robot Imitation Learning?

Can a robot learn more from the same collection budget by revisiting the situations it gets wrong? This episode explains what Mulligan demonstrates about focused robot data collection—and why better learning per episode is not automatically better learning per operator hour. We examine three questions: how failed-state retries and counterfactual demonstrations differ from ordinary DAgger corrections; what value-based action selection adds beyond the imitation policy; and how to interpret the blinded, paired real-robot results. The experiments cover marker insertion, nut placement, and cable routing on a Franka Panda, alongside simulated square-nut insertion. We also unpack a consequential simulation caveat: some collection rounds use feedback from the fixed evaluation grid to guide training-state selection. The practical discussion focuses on failure logging, coverage-only controls, independent acceptance tests, and measuring human time rather than treating every supervised episode as equally expensive. The release offers substantial reproducibility support, but a fresh human-in-the-loop campaign remains a new experiment. Before investing in another policy architecture, should you change which failures your operator teaches next?