Robot manipulation datasets have grown far slower than the models trained on them, mostly because collection stays closed and centralized. Expert operators gather demonstrations on lab hardware, process them offline, and ship a fixed benchmark that never grows again. A research team from Axis Robotics, UC Berkeley, Georgia Tech, NTU… is proposing a different shape for the problem. Their system, AXIS, moves demonstration collection into the browser, sends everything else to backend GPUs, and treats the dataset as something that keeps expanding rather than something that ships once.
Is it deployable? Partially. The training code is public as a patch layer over OpenPI, and the teleoperation platform is live in any browser. The dataset on Hugging Face is gated at 2.36 TB and restricted to non-commercial academic use. No policy checkpoints are released.
The browser and backend split
The core system decision is asymmetry. Contributors teleoperate a Franka Research 3 with a parallel-jaw gripper inside a MuJoCo WebAssembly frontend, using keyboard, mouse, virtual joystick or gamepad. Physics stepping and Three.js rendering run off the React UI thread, so logged state-action samples stay aligned with the simulator rather than the interface. Everything expensive happens elsewhere: rendering on 8x RTX 4090 GPUs, training and evaluation on 8x A100 GPUs.
