240 rollouts lift robot assembly from 24% to 88%

The result matters less as a single benchmark than as a signal about where robotics data budgets do the most work. The Dream Machines team collected teleoperation data across multiple desks using Gello controllers and virtual reality headsets, then found that data quality and visual diversity improved reliability faster than simply recording dozens of repetitive demonstration hours. That is the interesting part: the expensive thing in industrial automation has long been collecting more and more demonstration data, and this outcome points the other way.

The gains came from introducing human corrections into the training pipeline, with 240 recovery rollouts doing the heavy lifting. Full model fine-tuning outperformed low-rank adaptation methods on physical success rates, and Real Time Chunking settings from Physical Intelligence pushed final insertion success to ninety-eight percent during evaluations.

For companies developing robotic automation, the practical reading is that spending on edge-case interventions and execution blending parameters may buy more reliability than volume. Whether the 88% and ninety-eight percent figures carry into different tasks, hardware, or less controlled environments is the open question, since these came from the team's own physical evaluations.