Robot Learning
Mobile ALOHA
Mobile ALOHA is a low-cost mobile bimanual manipulation system from Stanford, introduced in 2024, that mounts an ALOHA dual-arm teleoperation rig on a wheeled base which the operator drives by walking while tethered to it, enabling whole-body demonstrations. Policies trained with imitation learning on 50 demonstrations per task, co-trained on static ALOHA data, performed household tasks such as cooking shrimp, calling an elevator, and wiping spills.
Why it matters for physical AI
Whole-body household tasks were long considered out of reach for imitation learning; demonstrating them with tens of demonstrations on sub-$32,000 hardware reset expectations for data-driven mobile manipulation.
Build physical AI
Put these concepts to work on real hardware
Axol is a dual-arm robot built for physical AI — teleoperate it, collect demonstrations, and deploy learned policies out of the box.