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arXiv · 2611.04083

MABEL: An Open Anthropomorphic Mobile Bimanual Platform for Teleoperation and Learning

Qilong Cheng·Rooholla Khorrambakht·Mohamed Yassine Kabouri·Quang Nam Nguyen·Luca Macesanu·Haoran Zhou·Yipeng Wang·Ludovic Righetti
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Abstract

As robot learning moves beyond the tabletop, many tasks require mobility, adjustable reach, active perception, and bimanual dexterity. Commercial platforms combining these capabilities are costly, while affordable open-source alternatives cover only a subset and require software integration. We present MABEL, an open-source anthropomorphic mobile bimanual robot with 56 degrees of freedom (DOF) and a US$8.1k parts cost. It combines two 7-DOF arms with 17-DOF hands, a pitching torso, a lift, a 3-DOF neck, and a holonomic base. Back-drivable arms and torso support compliant interaction, with fingertip reach from floor level to 2.23 m. The head and torso roughly triple modeled camera coverage relative to a torso-fixed camera. Apple Vision Pro head and hand tracking enables whole-body teleoperation without handheld controllers, with 137 ms mean glass-to-glass stereo video latency over the robot's Wi-Fi hotspot. We demonstrate an end-to-end imitation learning pipeline, from teleoperated data collection to policy deployment, using a shared command interface and whole-body controller.

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Qilong Cheng, Rooholla Khorrambakht, Mohamed Yassine Kabouri, Quang Nam Nguyen, Luca Macesanu, Haoran Zhou, Yipeng Wang, Ludovic Righetti. 2026-11-02. MABEL: An Open Anthropomorphic Mobile Bimanual Platform for Teleoperation and Learning. https://arxiv.org/abs/2611.04083

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