As the population continues to age, a shortage of caregivers is expected in the future. Dressing assistance, in particular, is crucial for opportunities for social participation. Especially dressing close-fitting garments, such as socks, remains challenging due to the need for fine force adjustments to handle the friction or snagging against the skin, while considering the shape and position of the garment. This study introduces a method uses multi-modal information including not only robot's camera images, joint angles, joint torques, but also tactile forces for proper force interaction that can adapt to individual differences in humans. Furthermore, by introducing semantic information based on object concepts, rather than relying solely on RGB data, it can be generalized to unseen feet and background. In addition, incorporating depth data helps infer relative spatial relationship between the sock and the foot. To validate its capability for semantic object conceptualization and to ensure safety, training data were collected using a mannequin, and subsequent experiments were conducted with human subjects. In experiments, the robot successfully adapted to previously unseen human feet and was able to put socks on 10 participants, achieving a higher success rate than Action Chunking with Transformer and Diffusion Policy. These results demonstrate that the proposed model can estimate the state of both the garment and the foot, enabling precise dressing assistance for close-fitting garments.

翻译：随着人口持续老龄化，未来预计将出现护理人员短缺的问题。衣物穿着辅助对于社会参与机会尤为重要。特别是紧身衣物（如袜子）的穿着，由于需要精细的力调节以处理与皮肤间的摩擦或勾挂，同时需考虑衣物的形状与位置，目前仍具挑战性。本研究提出一种方法，利用多模态信息（不仅包括机器人摄像头图像、关节角度、关节扭矩，还包括触觉力）实现能够适应个体差异的适当力交互。进一步地，通过引入基于物体概念的语义信息（而非仅依赖RGB数据），该方法可泛化至未见过的足部与背景场景。此外，结合深度数据有助于推断袜子与足部间的相对空间关系。为验证其语义物体概念化能力并确保安全性，训练数据通过人体模型采集，后续实验则在真人受试者上进行。实验结果表明，机器人成功适应了先前未接触的人类足部，并为10名参与者完成了袜子穿着，其成功率高于基于Transformer的动作分块方法与扩散策略。这些结果证明，所提模型能够同时估计衣物与足部状态，从而实现对紧身衣物的精准穿着辅助。