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ALOHA (A Low-cost Open-source Hardware system for Bimanual Teleoperation) is a stationary tabletop leader-follower rig from Stanford (Tony Z. Zhao et al.), built for fine-grained imitation learning. Two ViperX 300 6DOF follower arms are puppeteered by two WidowX leader arms; a human operates the leaders and the followers mirror the motion while multi-view cameras record demonstrations. Paired with the ACT (Action Chunking with Transformers) policy, it became the most-cited low-cost bimanual data-collection platform from 2023 onward. The design is fully open-source (3D-print parts, assembly tutorial, training code) and is also sold as a kit by Trossen Robotics for roughly a $20k build budget.
Listed Jul 2026
~$20k DIY build budget (open-source BOM; ~$15k–22k depending on arms/sourcing). No purchasable list price.
Observed price, not an offer.
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ALOHA is a foundational open-source bimanual manipulation platform introduced at RSS 2023 (arXiv:2304.13705). It uses a leader-follower (puppeteering) teleoperation scheme: an operator physically moves two low-cost WidowX leader arms, and two ViperX 300 6DOF follower arms replicate the motion in real time. Four RGB cameras (two stationary, two wrist-mounted) stream synchronized video for demonstration collection, which is then used to train ACT imitation-learning policies capable of precise, contact-rich tasks. The system is deliberately low-cost and reproducible from 3D-printed parts plus off-the-shelf Trossen/Interbotix arms, and is distributed both as open hardware and as a purchasable Trossen ALOHA kit.
Derived from its documented connections — mechanical, electrical, software.
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