机器人
杠杆(统计)
管道(软件)
人工智能
抓住
人机交互
计算机科学
模拟
对象(语法)
工程类
计算机视觉
软件工程
程序设计语言
作者
Fan Zhang,Yiannis Demiris
出处
期刊:Science robotics
[American Association for the Advancement of Science (AAAS)]
日期:2022-04-06
卷期号:7 (65)
被引量:37
标识
DOI:10.1126/scirobotics.abm6010
摘要
Assistive robots have the potential to support people with disabilities in a variety of activities of daily living, such as dressing. People who have completely lost their upper limb movement functionality may benefit from robot-assisted dressing, which involves complex deformable garment manipulation. Here, we report a dressing pipeline intended for these people and experimentally validate it on a medical training manikin. The pipeline is composed of the robot grasping a hospital gown hung on a rail, fully unfolding the gown, navigating around a bed, and lifting up the user's arms in sequence to finally dress the user. To automate this pipeline, we address two fundamental challenges: first, learning manipulation policies to bring the garment from an uncertain state into a configuration that facilitates robust dressing; second, transferring the deformable object manipulation policies learned in simulation to real world to leverage cost-effective data generation. We tackle the first challenge by proposing an active pre-grasp manipulation approach that learns to isolate the garment grasping area before grasping. The approach combines prehensile and nonprehensile actions and thus alleviates grasping-only behavioral uncertainties. For the second challenge, we bridge the sim-to-real gap of deformable object policy transfer by approximating the simulator to real-world garment physics. A contrastive neural network is introduced to compare pairs of real and simulated garment observations, measure their physical similarity, and account for simulator parameters inaccuracies. The proposed method enables a dual-arm robot to put back-opening hospital gowns onto a medical manikin with a success rate of more than 90%.
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