  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

Robotics;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning;Systems and Control;Systems and Control