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%.

中文翻译：

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学习机器人辅助穿衣的服装操作策略

辅助机器人有可能在各种日常生活活动中为残疾人提供支持，例如穿衣。完全失去上肢运动功能的人可能会受益于机器人辅助穿衣，这涉及复杂的可变形服装操作。在这里，我们报告了一个适用于这些人的敷料管道，并在医学培训人体模型上进行了实验验证。该流水线由机器人抓取挂在栏杆上的病号服，将病衣完全展开，在病床周围导航，依次举起用户的手臂，最终为用户穿上衣服组成。为了使这条管道自动化，我们解决了两个基本挑战：首先，学习操作策略，将服装从不确定状态转变为有助于稳健穿着的配置；第二，将在模拟中学到的可变形对象操作策略转移到现实世界，以利用具有成本效益的数据生成。我们通过提出一种主动的预抓取操作方法来解决第一个挑战，该方法学习在抓取之前隔离服装抓取区域。该方法结合了可抓握和不可抓握的动作，从而减轻了仅抓握行为的不确定性。对于第二个挑战，我们通过将模拟器近似于现实世界的服装物理来弥合可变形对象策略转移的模拟到现实的差距。引入了对比神经网络来比较成对的真实和模拟服装观察结果，测量它们的物理相似性，并解释模拟器参数的不准确性。