A robot dataset gets more useful when its parts answer one another. That is what stood out to me in the data engine from @axisrobotics. Task generation decides which scenario and atomic skill need coverage. Contributors produce trajectories through browser teleoperation or real-world egocentric capture. A unified pipeline then verifies, cleans, annotates and augments those inputs before they become training data. The loop does not have to end there. Once a policy is trained, its weak spots can determine which tasks reopen for post-training. A contributor watches the policy attempt the task, takes over when it slips, then hands control back. Those corrections feed the next iteration. Each component has a separate job, but the value is in the feedback between them: generate, collect, process, train, expose a weakness, collect again. More data alone is a vague goal. A system that can ask for the next useful piece of data is a much sharper one. Start here: https://t.co/SUKXsZDwvC