Iknows (✱,✱)

Axis Weekly This week, we focused on making the robotics data loop more measurable and reproducible: separating real user signals from bot traffic, expanding TaskGen into articulated-object tasks, and turning data-to-model workflows into repeatable services. Key updates: - Data quality: Task 805’s high failure rate was driven by bots, not real players. - TaskGen: Codebase delivered for an upcoming update that will support end-to-end generation of articulated-object tasks from prompts. - Simulation and data infra: Asset bugs fixed, and the automated recover-from-failure pipeline is nearing full deployment. - Model training: Achieved a ~40% success rate in cross-simulation evaluation (IsaacLab to MuJoCo). - Sim-to-real: Updated the domain randomization roadmap to heavily boost physical parameter diversity. A closer look at this week’s progress 🧵

Axis Weekly This week, we continued strengthening our closed-loop robotics data pipeline, from TaskGen and simulation infrastructure to failure recovery and asset-level augmentation. Key updates: - Task generation: We completed asset scan and merged it into TaskGen, helping generated tasks reason over available assets, scene layouts, long-horizon workflows, and multi-embodiment settings. - Simulation infra: We improved MuJoCo verify, replay, and scene-variant workflows, with fixes around repeated downloads, caching, compatibility, and long-horizon multi-asset task stability. - Robot controls: We cleaned up gripper behavior, IK, teleoperation, and the control panel based on feedback from longer-horizon and multi-asset tasks. Failure recovery: We continued building a pipeline to turn failed and near-failed grasping states into reusable data for recovery learning. - Asset augmentation: With academic collaborators, we advanced a shape augmentation direction that can expand one seed asset into many physically plausible object variants. A closer look at this week’s progress 🧵

Giveaway will end tmr

5.25.26

5.25.26