SE3 Labs runs robot policies on real hardware for the teams training them, then turns observed failures into data for the next update. A team uploads a model checkpoint or connects an inference endpoint, defines the task, test environment, and success criteria, and SE3 executes the policy on standardized workcells in labs, factories, and other operating sites. The company handles the robots, sensors, task setup, safety, and scheduling, and it uses the same evaluation protocol across stations and policy versions so results stay comparable. This launch is for model teams that can already produce a strong demo and still cannot count on that policy once it leaves the lab or simulation. SE3 starts with tasks from industry partners, and it has opened remote evaluation on bimanual YAM stations, including ten real-world versions of RoboDojo simulation tasks. A policy runs until it fails or reaches a safety boundary. An operator then finishes the task, and SE3 keeps that correction as a continuous trajectory. The rollouts come back synchronized and quality-checked, segmented by task and outcome, with language annotation and hand and action tracking, so the next checkpoint can be scored on the same setup. Published runs on that stack already show where current policies stall. Across 100 zero-shot episodes of Dream on the RoboDojo tasks, no episode completed the full task, and the mean milestone score was 8 out of 100. On a separate LEGO placement task, a frozen code policy completed 6 of 20 placements, while a controller that kept the model in the loop completed 18 of 20. The San Francisco company, an NVIDIA Inception member backed by Y Combinator, is taking the same loop to partner sites as physical sandboxes, so policies can practice the tasks and conditions they will face in use. The launch post invites people to meet them at CoRL.