Bottom Dressing by a Dual-Arm Robot Using a Clothing State Estimation Based on Dynamic Shape Changes

Yamazaki, Kimitoshi; Oya, Ryosuke; Nagahama, Kotaro; Okada, Kei; Inaba, Masayuki

doi:10.5772/61930

Cited by 21 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…for robot-assisted dressing Yamazaki et al (2016) presented a system for a robot to assist users with pulling on trousers. Their work focused on using vision to estimate the state of the garment, of the person's body, and of the dressing task.…”

Section: Perceiving and Modeling The Usermentioning

confidence: 99%

Personalized collaborative plans for robot-assisted dressing via optimization and simulation

et al. 2019

View full text Add to dashboard Cite

Robots could be a valuable tool for helping with dressing but determining how a robot and a person with disabilities can collaborate to complete the task is challenging. We present task optimization of robot-assisted dressing (TOORAD), a method for generating a plan that consists of actions for both the robot and the person. TOORAD uses a multilevel optimization framework with heterogeneous simulations. The simulations model the physical interactions between the garment and the person being dressed, as well as the geometry and kinematics of the robot, human, and environment. Notably, the models for the human are personalized for an individual's geometry and physical capabilities. TOORAD searches over a constrained action space that interleaves the motions of the person and the robot with the person remaining still when the robot moves and vice versa. In order to adapt to real-world variation, TOORAD incorporates a measure of robot dexterity in its optimization, and the robot senses the person's body with a capacitive sensor to adapt its planned end effector trajectories. To evaluate TOORAD and gain insight into robot-assisted dressing, we conducted a study with six participants with physical disabilities who have difficulty dressing themselves. In the first session, we created models of the participants and surveyed their needs, capabilities, and views on robot-assisted dressing. TOORAD then found personalized plans and generated instructional visualizations for four of the participants, who returned for a second session during which they successfully put on both sleeves of a hospital gown with assistance from the robot. Overall, our work demonstrates the feasibility of generating personalized plans for robot-assisted dressing via optimization and physics-based simulation.

show abstract

Section: Perceiving and Modeling The Usermentioning

confidence: 99%

Personalized collaborative plans for robot-assisted dressing via optimization and simulation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Some recent work has also tackled the task of assisted dressing, where a person with limited mobility would be helped by a robotic assistant in the everyday task of dressing up. The system of Yamazaki et al [66] helps a sitting person put on a pair of pants. They used the optical flow to estimate the state of the clothing and developed a path planning algorithm that adapts to the length, size and position of the person's legs.…”

Section: Planar Objectsmentioning

confidence: 99%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

View full text Add to dashboard Cite

This paper aims to provide a comprehensive survey of recent advancements in modelling and autonomous manipulation of non-rigid objects. It first summarizes the recent advances in sensing and modelling of such objects with a focus on describing the methods and technologies used to measure their shape and estimate their material and physical properties. Formal representations considered to predict the deformation resulting from manipulation of non-rigid objects are then investigated. The third part provides a survey of planning and control strategies exploited to operate dexterous robotic systems while performing various tasks on objects made of different non-rigid materials.

show abstract

“…wrinkles) are irrelevant to perform most tasks. K. Yamazaki et al [11] focused on developing a vision-based state-estimator for the materials and learning the mappings between robot motions and material states and were able to perform lower-body dressing on a mannequin.…”

Section: Related Workmentioning

confidence: 99%

A framework for robot-assisted doffing of personal protective equipment

Umali

Berenson

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

When treating highly-infectious diseases such as Ebola, health workers are at high risk of infection during the doffing of Personal Protective Equipment (PPE). This is due to factors such as fatigue, hastiness, and inconsistency in training. The introduction of a semi-autonomous robot doffing assistant has the potential to increase the safety of the doffing procedure by assisting the human during high-risk subtasks. The addition of a robot into the procedure introduces the need to transform a purely human task into a sequence of safe and effective human-robot collaborative actions. We take advantage of the fact that the human can do the more intricate motions during the procedure. Since diseases like Ebola can spread through the mucous membranes of the eyes, ears, nose, and mouth our goal is to keep the human's hands away from his or her face as much as possible. Thus our framework focuses on using the robot to help avoid such human risky motion. As secondary goals, we seek to also minimize the human's effort and make the robot's motion intuitive for the human. To address different versions and variants of PPE, we propose a way of segmenting the doffing procedure into a sequence of human and robot actions such that the robot only assists when necessary. Our framework then synthesizes assistive motions for the robot that perform parts of the tasks according to the metrics above. Our experiments on five doffing tasks suggest that the introduction of a robot assistant improves the safety of the procedure in three out of four of the high-risk doffing tasks while reducing effort in all five tasks.

show abstract

Bottom Dressing by a Dual-Arm Robot Using a Clothing State Estimation Based on Dynamic Shape Changes

Cited by 21 publications

References 18 publications

Personalized collaborative plans for robot-assisted dressing via optimization and simulation

Personalized collaborative plans for robot-assisted dressing via optimization and simulation

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

A framework for robot-assisted doffing of personal protective equipment

Contact Info

Product

Resources

About