Close-Fitting Dressing Assistance Based on State Estimation of Feet and Garments with Semantic-based Visual Attention

TL;DR

This paper presents a novel multi-modal, semantic-aware approach for robotic dressing assistance that effectively adapts to individual differences and unseen cases, improving success rates in close-fitting garment tasks.

Contribution

It introduces a semantic-based visual attention method combined with multi-modal data for improved state estimation in dressing robots, enhancing adaptability and safety.

Findings

Successfully dressed 10 participants with higher success rate than baseline methods

Achieved accurate state estimation of garments and feet using multi-modal and semantic data

Demonstrated robustness to unseen human feet and backgrounds

Abstract

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.