Axis Weekly Last week, we made progress across the full robotics data loop, including task generation, simulation infrastructure, model training, and failure recovery. Key updates: - Task generation: We improved TaskGen with better automatic checker generation, stronger multi-embodiment support, and more efficient domain randomization to scale task diversity with less manual design effort. - Simulation infra: We continued improving MuJoCo verify/replay and scene-variant workflows, including fixes across data collection, multi-asset scenes, repeated loading/downloads, initial states, teleoperation, IK, and gripper control. - Model training: We confirmed that the new randomized tasks are learnable with sufficient data. In our current experiment, 500 demos successfully produced an executable policy, while 100 demos were not enough. - Failure recovery: We began building a recover-from-failure pipeline to collect and categorize gripper failure and near-failure states during grasping, which will later support more robust recovery policy learning. A closer look at this week’s progress🧵