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Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

2024/01/04 by Zipeng Fu, Fu, Zipeng, Tony Z. Zhao +3 · 2 voices · 201 citations
Computer Science · Engineering · Medicine · Psychology · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Robot Manipulation and Learning #Robotics (cs.RO) #Social Robot Interaction and HRI #Stroke Rehabilitation and Recovery #Systems and Control (eess.SY) #cs.AI #cs.CV #cs.LG #cs.RO #eess.SY #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2401.02117

openalex publication_date 2024/01/04 · arxiv published 2024/01/04 · arxiv updated 2024/01/04 · openalex created_date 2024/01/08 · openalex updated_date 2026/07/28

Abstract

Imitation learning from human demonstrations has shown impressive performance in robotics. However, most results focus on table-top manipulation, lacking the mobility and dexterity necessary for generally useful tasks. In this work, we develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control. We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection. It augments the ALOHA system with a mobile base, and a whole-body teleoperation interface. Using data collected with Mobile ALOHA, we then perform supervised behavior cloning and find that co-training with existing static ALOHA datasets boosts performance on mobile manipulation tasks. With 50 demonstrations for each task, co-training can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously complete complex mobile manipulation tasks such as sauteing and serving a piece of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling and entering an elevator, and lightly rinsing a used pan using a kitchen faucet. Project website: https://mobile-aloha.github.io

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