Chelsea Finn shares Mulligan paper: the more reliable the robot, the less efficient DAgger data collection becomes

Chelsea Finn points out that as robots become more reliable, DAgger-style data collection efficiency drops sharply; for example, a 90% success rate means a human spends more than 90% of the time as a passive observer. The Mulligan paper she cites proposes the key idea that failures often come from similar initial states, so learning can be focused on these states; in deployment, the robot performs the task and the human intervenes when needed, with each collection round focusing on the initial states where the robot fails. Across 3 real tasks and 2,550 blind tests, it outperformed uniform collection under the same budget. Paper: https://arxiv.org/abs/2610.05882 .