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Mobile ALOHA is Stanford's low-cost whole-body bimanual teleoperation and imitation-learning reference design: an ALOHA-style leader-follower dual-arm rig (2 leader + 2 follower 6-DOF arms) mounted on a wheeled mobile base, letting a single operator puppeteer both arms and drive the base simultaneously to collect demonstrations for mobile manipulation tasks (cooking, dishwashing, taking an elevator). Co-training with the earlier stationary ALOHA dataset raises success rates by up to ~90%. Hardware BOM and ACT training code are open-source.
~$32 · Listed Jul 2026
000 reported hardware build cost (open-source platform; est. range $28k-35k)
Observed price, not an offer.
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Mobile ALOHA (Fu, Zhao, Finn — Stanford, CoRL 2024) extends the stationary ALOHA teleoperation system with a wheeled mobile base for whole-body data collection. The operator physically backdrives two leader arms while the two follower arms mirror the motion, and moves with the base tethered at the waist, so arm and base trajectories are captured together. The system uses Interbotix arms (WidowX-250 6-DOF leaders, ViperX-300 6-DOF followers) on an AgileX Tracer-style base with consumer webcams and an onboard laptop GPU. It is a published open-hardware reference design, not a commercial SKU: the value is the whole-body teleop interface plus the co-training recipe that lets a handful of mobile demos, combined with stationary ALOHA data, learn tasks like cooking shrimp, calling an elevator, and rinsing a pan.
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