Axis Robotics
@axisrobotics
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Scale Physical AI for the real world. Robot intelligence is not built by a few; it's built by all.
axisrobotics.ai
Joined November 2025
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.
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6/6 In short, we are building a more valuable data distribution: from single-step actions to multi-stage tasks, from single-arm manipulation to bimanual coordination, and from a single robot embodiment to cross-embodiment adaptation. This direction brings us closer to a data
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5/6 From a product and strategy perspective, Axis’s data capability is expanding in two directions at the same time. On one hand, we continue to improve data diversity by covering more scenes, assets, layouts, and visual variations. On the other hand, we are also increasing
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4/6 Cross-embodiment tasks further expand the value of the data. By supporting bimanual teleoperation and adaptation across different robot embodiments, Axis is moving from single-robot datasets toward a multi-embodiment, multi-control-mode data system. This is critical for
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3/6 Axis’s task system also supports continuous success detection and staged checkers. A complex long-horizon task can be decomposed into multiple well-defined subgoals, such as grasping an object, moving it to a target region, placing it correctly, or closing a container. Each
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2/6 Long-horizon tasks are especially well-suited to simulation-based data collection. In the real world, once a long-horizon task fails midway, resetting the environment, restoring object states, and restarting the collection process can be costly and time-consuming. In
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1/6 This marks a shift in what we collect. Compared with earlier manipulation tasks, long-horizon and bimanual tasks involve more stages, stronger temporal structure, and higher demands on planning, coordination, and recovery. This is not only an expansion in data volume, but
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We recently launched a new set of robotic data collection tasks, with a focus on long-horizon tasks (LH) and cross-embodiment tasks (Multi Embodiment). These include bimanual teleoperation and task adaptation across different robot morphologies. Why this matters: 1. Axis is
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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 ↓
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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,
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Task availability: SN/04 is rolling out gradually. At launch, around 10 BitRobot collaboration tasks will be available each day, with more tasks expected to come online over time. BitRobot collaboration tasks and regular Axis tasks will be released daily at 8:00 PM GMT+8 /
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How to join: To join BitRobot collaboration tasks on Axis, users will need an access code. A total of 1,000 access codes will be released in the first batch: 500 for the BitRobot community and 500 for loyal Axis community members. All Axis community members who hold all X, Y,
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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
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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
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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
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On the TaskGen side, articulated-object generation has been merged into the automatic generation pipeline. TaskGen now supports objects such as cabinets, dishwashers, and drawers, while staying compatible with the existing randomization workflow. 27 tasks are ready for
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