Axis Robotics
@axisrobotics • 24,796 subscribers
Scale Physical AI for the real world. Robot intelligence is not built by a few; it's built by all.
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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:
Axis Robotics223,115 Aufrufe • vor 2 Monaten
At Axis, every trajectory submitted by our community undergoes a strict replay validation process. We run each submission through checker to verify whether the target task was successfully completed. To see how strict it is, check this demo (Task: Place The Toy Train On The Board Game Box). Real human data passes smoothly (Video 1). However, bots or manually altered data will fail (Video 2). Why? Faking numbers breaks the simulation's physics causality. Even tiny tweaks cause error accumulation, resulting in failed movements. This invalid data is automatically rejected. Because of this mechanism, data submitted via bots will ultimately fail our replay verification. Invalid data is strictly excluded from model training, and the task slot is reopened to the community to collect genuine, high-quality trajectories. Furthermore, we actively monitor for duplicated data. Trajectories that are identical lack the diversity required for robot learning and will not be credited by our scoring system. If we detect accounts submitting a massive volume of identical trajectories, all associated addresses will be permanently banned. For Axis, the quality and diversity of data are the only ways to solve the robotics generalization gap. They will always be our absolute top priorities.
Axis Robotics30,447 Aufrufe • vor 1 Monat
Domain Randomization (DR) is a key component of the data augmentation pipeline at Axis Robotics. By applying DR, we are able to scale verified, high-quality human trajectories by 10x to 100x. During training, we systematically introduce variances in environmental parameters. This prevents the model from relying on spurious visual correlations. The objective is to ensure the policy learns rather than overfitting. To demonstrate the necessity and effectiveness of this approach, we evaluated both DR and No-DR models on Task 74 (pour_water_into_mug). The empirical results show a definitive impact on real-world deployment reliability: integrating DR into the pipeline increased the success rate from 0% to 90% (Fig. 1). This divergence stems from how the respective policies process visual observations (Fig. 2). The baseline (No DR) model overfits to the static visual background. It essentially memorizes the poses from the training dataset but fails to generalize when subjected to the inevitable variances of real-world deployment. Consequently, it cannot execute the correct manipulation on the target object. Conversely, the DR-trained model learns to extract essential geometric features and physical constraints, filtering out superficial visual noise. This leads to significantly higher robustness in dynamic environments. The structural difference in execution is clearly reflected in the end-effector trajectory data: These real-world deployment recordings further illustrate this difference (Videos 1 and 2). Scaling Physical AI requires turning raw trajectory data into robust policies, and a rigorously engineered DR infrastructure is an essential bridge to close the Sim2Real gap.
Axis Robotics27,125 Aufrufe • vor 1 Monat
Every roboticist knows the pain of "Day 1." Real-world training: ❌ Fragile hardware & constant failures ❌ High latency & slow iteration ❌ Narrow data diversity Axis is breaking the cycle. We’ve built the first browser-based, infra-level platform that decouples assets, tasks, and high-fidelity rendering. Through our protocol-level abstraction, we’re moving beyond simple simulation to a unified data engine: ✅ Low-barrier collection with infinite data diversity. ✅ Cross-simulator operations, unified. ✅ End-to-end acceleration: Task -> Data -> Train -> Deployment in one loop. Coming soon.
Axis Robotics39,215 Aufrufe • vor 4 Monaten
Axis Product Update The Precision & Security Patch is here. - Hardened Anti-Cheat: Deployed backend trajectory replay verification and end-state DB checks. Bots and invalid runs are strictly filtered. - Wrist-Cam View: Added a direct camera feed from the robot's wrist for precise manipulation. - Custom Sensitivity: A new UI slider to fine-tune your teleoperation speed and handling. Better visibility for you, verified intelligence for the network.
Axis Robotics18,667 Aufrufe • vor 3 Monaten
10,000 valid trajectories collected — and we did it ahead of schedule, in just 5 days. We’ve reached our data milestone for "Little Prince's Rose." This community-driven dataset is now entering the pipeline for cleaning, augmentation, and model training, advancing the intelligence of Franka at this very moment. While the Discord role claiming is closing, the experiment will remain open as a welcoming portal for everyone to experience the Axis and how we tackle the data bottleneck for robotics. Robot intelligence is not built by a few; it's built by all.🌹
Axis Robotics23,301 Aufrufe • vor 4 Monaten
Hot take: Robots aren't born in factories. They're born in our imagination. Refined in games. Trained in simulations. Tested in reality. Then reality feeds back into imagination. Sim to real to sim—not a shortcut, but a loop. And the loop doesn’t stop. The beginning of infinity, from Axis Robotics
Axis Robotics14,109 Aufrufe • vor 5 Monaten
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