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Axis Robotics

@axisrobotics • 25,308 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 Launch Recap: Momentum & Upgrades 🔵 Another massive week for Axis! Since our official launch, the community's energy has been incredible, and the numbers speak for themselves: Growth since launch: - New Tasks Released: 55 - New Contributors: 11,474 - New Contributions: 137,395 - Active Simulation Time: ~4,390 hours Since launch, we have shipped: 1. Upgraded Gripping System: Fine-tuned friction and strength for a more lifelike, reliable grasp. 2. Expanded Teleoperation UI: Rescaled the workspace for a wider, more immersive field of view. 3. Fixed Release Schedule: Standardized daily new task drops at 12:00 PM UTC. 4. Infra Scaling: Upgraded our backend to handle high concurrency and massive traffic smoothly. We are just getting started (✱,✱)

Axis Launch Recap: Momentum & Upgrades 🔵 Another massive week for Axis! Since our official launch, the community's energy has been incredible, and the numbers speak for themselves: Growth since launch: - New Tasks Released: 55 - New Contributors: 11,474 - New Contributions: 137,395 - Active Simulation Time: ~4,390 hours Since launch, we have shipped: 1. Upgraded Gripping System: Fine-tuned friction and strength for a more lifelike, reliable grasp. 2. Expanded Teleoperation UI: Rescaled the workspace for a wider, more immersive field of view. 3. Fixed Release Schedule: Standardized daily new task drops at 12:00 PM UTC. 4. Infra Scaling: Upgraded our backend to handle high concurrency and massive traffic smoothly. We are just getting started (✱,✱)

29,432 görüntüleme

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

27,858 görüntüleme

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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 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:

251,262 görüntüleme • 4 ay önce

HCMC showed up! 🇻🇳 Builders came together to swap ideas, spark conversations, and dive into the possibilities of Physical AI. The energy in the room was unreal. We’re grateful to be growing a community that’s curious, ambitious, and ready to build the future together. Watch the recap below. 🎥

HCMC showed up! 🇻🇳 Builders came together to swap ideas, spark conversations, and dive into the possibilities of Physical AI. The energy in the room was unreal. We’re grateful to be growing a community that’s curious, ambitious, and ready to build the future together. Watch the recap below. 🎥

15,432 görüntüleme • 13 gün önce

Click-and-Drag Gripper Control is now LIVE We've been simplifying high-hardware-demand, complex simulation teleoperation through web-based keyboard-and-mouse control — and now we're taking it one step further. Direct click-and-drag gripper control is officially here. This eliminates a significant number of intermediate steps. Simply reorient your viewing plane by adjusting the camera perspective, and focus only on gripper-object contact. See it in action 👇

Click-and-Drag Gripper Control is now LIVE We've been simplifying high-hardware-demand, complex simulation teleoperation through web-based keyboard-and-mouse control — and now we're taking it one step further. Direct click-and-drag gripper control is officially here. This eliminates a significant number of intermediate steps. Simply reorient your viewing plane by adjusting the camera perspective, and focus only on gripper-object contact. See it in action 👇

29,374 görüntüleme • 1 ay önce

Double-Click Auto-Navigate & Save/Rewind are LIVE! Following last week's click-and-drag gripper control, here's what's new: Double-click any object and the nearest arm automatically moves into position. - Each movement is randomized, so every approach adds training diversity. - Works for both single-arm and dual-arm tasks. We also shipped Save & Rewind. - Press N to save a checkpoint at any critical moment during a task. The checkpoint indicator in the top-right corner will light up to confirm. - Press B to instantly roll back to your saved state if anything goes wrong. That means you can now drag arms into place, double-click to auto-navigate to any object, and save checkpoints on the fly — all directly from your browser. Smoothest simulation teleop UX, only on Axis. Try it out now!

Double-Click Auto-Navigate & Save/Rewind are LIVE! Following last week's click-and-drag gripper control, here's what's new: Double-click any object and the nearest arm automatically moves into position. - Each movement is randomized, so every approach adds training diversity. - Works for both single-arm and dual-arm tasks. We also shipped Save & Rewind. - Press N to save a checkpoint at any critical moment during a task. The checkpoint indicator in the top-right corner will light up to confirm. - Press B to instantly roll back to your saved state if anything goes wrong. That means you can now drag arms into place, double-click to auto-navigate to any object, and save checkpoints on the fly — all directly from your browser. Smoothest simulation teleop UX, only on Axis. Try it out now!

13,005 görüntüleme • 21 gün önce

A few weeks ago, we shared our progress on articulated objects and long-horizon tasks. Here are two representative examples: - We've been steadily expanding our asset library to cover more articulated objects. Articulated objects have always been a challenging asset class to handle in simulation. Interacting with them requires robots to master atomic skills such as pushing, pulling, opening, and closing, and to understand part structure, interaction constraints, and how the object moves. - Long-horizon tasks can now be generated at scale. Long-horizon tasks are the other hard category: they require chaining multiple sub-goals in sequence. A failure early in the task can cascade and make the rest unrecoverable. Axis is scaling along three dimensions at once: data volume, data quality, and task difficulty.

A few weeks ago, we shared our progress on articulated objects and long-horizon tasks. Here are two representative examples: - We've been steadily expanding our asset library to cover more articulated objects. Articulated objects have always been a challenging asset class to handle in simulation. Interacting with them requires robots to master atomic skills such as pushing, pulling, opening, and closing, and to understand part structure, interaction constraints, and how the object moves. - Long-horizon tasks can now be generated at scale. Long-horizon tasks are the other hard category: they require chaining multiple sub-goals in sequence. A failure early in the task can cascade and make the rest unrecoverable. Axis is scaling along three dimensions at once: data volume, data quality, and task difficulty.

13,478 görüntüleme • 27 gün önce

The Policy Checker Page is LIVE! Remember we talked about showing our intermediate model and success heatmap? You can now check them live on our hub! For each intermediate policy, you can: - View its summary — success rate, task type, checkpoints, and more. - Run it directly in your browser to observe its real-time inference. (If it looks a bit choppy, that's expected — the model is inferring live!) - Explore its success heatmap — showing the initial states under which the policy successfully completes the task. This gives the community a lens into our backend policy training, and lays the groundwork for better demonstrating our recover from failure training loop going forward. How to enter ⬇️

The Policy Checker Page is LIVE! Remember we talked about showing our intermediate model and success heatmap? You can now check them live on our hub! For each intermediate policy, you can: - View its summary — success rate, task type, checkpoints, and more. - Run it directly in your browser to observe its real-time inference. (If it looks a bit choppy, that's expected — the model is inferring live!) - Explore its success heatmap — showing the initial states under which the policy successfully completes the task. This gives the community a lens into our backend policy training, and lays the groundwork for better demonstrating our recover from failure training loop going forward. How to enter ⬇️

12,915 görüntüleme • 28 gün önce

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.

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.

30,772 görüntüleme • 3 ay önce

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.

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.

27,125 görüntüleme • 3 ay önce

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.

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.

39,439 görüntüleme • 6 ay önce

A few more moments from MEMORY OF PIZZA in Jakarta. Good energy, good people, and memories worth replaying. RememberUS indodax Indonesia Blockchain Week 2026 Base Indonesia

A few more moments from MEMORY OF PIZZA in Jakarta. Good energy, good people, and memories worth replaying. RememberUS indodax Indonesia Blockchain Week 2026 Base Indonesia

12,559 görüntüleme • 1 ay önce

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.🌹

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.🌹

23,424 görüntüleme • 5 ay önce

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 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.

18,667 görüntüleme • 4 ay önce

Just getting started with Axis? We put together a quick video guide to walk you through the platform. Watch the tutorial, master the controls, and get ready for the new tasks.

Just getting started with Axis? We put together a quick video guide to walk you through the platform. Watch the tutorial, master the controls, and get ready for the new tasks.

16,522 görüntüleme • 3 ay önce

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

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

14,109 görüntüleme • 6 ay önce

Daha fazla içerik yok.