Axis is officially LIVE on @base. 🔵
Axis is scaling Physical AI for the real world, contributed by everyone.
You can control robots in a virtual world, generate training data at scale, and help build the brain behind tomorrow's robots. All from browser. No hardware needed.
Start building robotics intelligence today: https://t.co/xeQnZzV2WM
Announcing our collaboration with @BitRobotNetwork!
Axis is launching SN/04 on BitRobot, the open robotics lab on Solana that coordinates distributed contributors to accelerate Physical AI research.
SN/04 is a teleop-in-sim mission where contributors complete web-based robotics simulation tasks, generate valuable training data, and earn rewards from both ecosystems.
Together, we’re scaling human demonstrations for Physical AI — powered by everyone.
Rules and details below ↓
1/ We’re doubling down on teleop-in-sim data capture. SN/04 is now live on BitRobot with @axisrobotics.
Early users will get private access to train robots and earn rewards across both ecosystems.
Comment “gbot” if you want fast-track access ↓
Rewards:
Completed BitRobot collaboration tasks count as regular Axis tasks for badge rewards. For example, if you complete BitRobot collaboration tasks for 7 consecutive days, they will count toward the Creature of Habit badge.
In addition to Axis platform rewards, participants will also earn BitRobot points for valid contributions in collaboration tasks.
More details on BitRobot points and reward tracking will be shared through official channels.
User experience:
No Solana transaction signing is required. No SOL gas is needed.
The user flow stays the same as regular Axis tasks: sign in, complete the task, and sign.
Axis Robotics is actively supporting and collaborating with leading researchers in Physical AI.
If you’re at ICRA or CVPR this week, come connect with our advisors and team at the workshops.
See you in Vienna and Denver.
We are thrilled to sponsor the 3rd MEIS Workshop at CVPR 2026!
As Generative AI redefines Embodied Multi-Agent Systems, Axis Robotics is proud to support the researchers pushing the boundaries of multi-agent collaboration, simulation, and robustness.
🏆 Best Paper & Demo Awards (Cash + Recognition)
📅 Deadline: Apr 15, 2026
📍 Join us in Denver on June 3rd
Let’s build the future of collective intelligence together. Check out the details from Prof. Tu @_vztu below!
Axis Weekly
This week was about trust and transfer: making community data cleaner, generated tasks broader, and trained policies more robust as they move from simulation to real robots.
Key updates:
- Data quality: We completed a suspicious-user audit script to detect abnormal collection behavior using user statistics and replay/verify failure reasons.
- Webapp and simulation: We improved key gripper and asset interactions, including penetration, heavy-object grasping, and IK flexibility.
- Recover-from-failure: We tested Failure Task 892 and collected 300+ failure initial states, with the next round moving to repaired and more randomized tasks.
- TaskGen: Articulated-object generation is now merged into the automatic generation pipeline, covering cabinets, dishwashers, drawers, and existing randomization workflows.
- Model and real-world stack: We completed the first round of fine-tuning, evaluation, and benchmarking, merged the π0.5 evaluation pipeline into the real-world stack, and started bringing a new embodiment into the loop.
A closer look at this week’s progress 🧵
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 🧵
On the model side, we finished the first round of fine-tuning, evaluation, and benchmarking, and are now adjusting the data recipe for better performance.
The π0.5 evaluation pipeline has been merged into the real-world stack. Web policy inference can now load model checkpoints for online deployment, while RoboVerse experiments are studying dataset-size scaling and adding object geometry information into training.
We are also starting to bring a new embodiment into the physical validation loop. Details are still under wraps, but the goal is to connect our simulation-heavy training pipeline to broader real-world robot testing.
https://t.co/YX1xSSvhv5