Personalized Assistance for Dressing Users

Klee, Steven D.; Ferreira, Beatriz Quintino; Silva, Rui; Costeira, João Paulo; Melo, Francisco S.; Veloso, Manuela

doi:10.1007/978-3-319-25554-5_36

Cited by 50 publications

(35 citation statements)

References 15 publications

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“…Assistive robots can use the vision information of a human body when dressing a user [4][5][6]. However, occlusions could occur when the robot's arms, the clothes and the human body are in close contact, which leads to human pose recognition failures.…”

Section: Introductionmentioning

confidence: 99%

Iterative path optimisation for personalised dressing assistance using vision and force information

Gao¹,

Chang²,

Demiris³

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

104

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Abstract-We propose an online iterative path optimisation method to enable a Baxter humanoid robot to assist human users to dress. The robot searches for the optimal personalised dressing path using vision and force sensor information: vision information is used to recognise the human pose and model the movement space of upper-body joints; force sensor information is used for the robot to detect external force resistance and to locally adjust its motion. We propose a new stochastic path optimisation method based on adaptive moment estimation. We first compare the proposed method with other path optimisation algorithms on synthetic data. Experimental results show that the performance of the method achieves the smallest error with fewer iterations and less computation time. We also evaluate real-world data by enabling the Baxter robot to assist real human users with their dressing.

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Section: Introductionmentioning

confidence: 99%

Iterative path optimisation for personalised dressing assistance using vision and force information

Gao¹,

Chang²,

Demiris³

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

104

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“…Without mobility, robots are restricted to a narrow set of tasks and are unable to leave the immediate vicinity of the human to provide assistance elsewhere. Recent studies have introduced generalpurpose mobile manipulators for various assistive robotic tasks, including shaving [9,39], dressing [41,42,43], fetch-and-carry [44,45,46,47], and guiding tasks [48]. Our meal-assistance system has a mobile base that has the potential to enhance the quality of feeding assistance.…”

Section: Assistive Manipulatorsmentioning

confidence: 99%

Active robot-assisted feeding with a general-purpose mobile manipulator: Design, evaluation, and lessons learned

Park

Hoshi

Mahajan

et al. 2020

Robotics and Autonomous Systems

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Eating is an essential activity of daily living (ADL) for staying healthy and living at home independently. Although numerous assistive devices have been introduced, many people with disabilities are still restricted from independent eating due to the devices' physical or perceptual limitations. In this work, we present a new meal-assistance system and evaluations of this system with people with motor impairments. We also discuss learned lessons and design insights based on the evaluations. The meal-assistance system uses a general-purpose mobile manipulator, a Willow Garage PR2, which has the potential to serve as a versatile form of assistive technology. Our active feeding framework enables the robot to autonomously deliver food to the user's mouth, reducing the need for head movement by the user. The user interface, visually-guided behaviors, and safety tools allow people with severe motor impairments to successfully use the system. We evaluated our system with a total of 10 able-bodied participants and 9 participants with motor impairments. Both groups of participants successfully ate various foods using the system and reported high rates of success for the system's autonomous behaviors. In general, participants who operated the system reported that it was comfortable, safe, and easy-to-use.

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“…For example, Koganti et al [4] used RGB-D and motion capture data to estimate the topological relationship between a person's body and a garment. Klee et al [5] visually detected a person's pose which was used by a Baxter robot to assist in putting on a hat. Pignat et al [6] tracked a person's hand movement in real time using an AR tag.…”

Section: A Robot-assisted Dressing and Force Estimationmentioning

confidence: 99%

“…As a result, the robot would be unable to recognize or replan actions if the garment were to entirely miss a person's body. Future work could address this by incorporating other modalities, such as vision-based techniques, to estimate a person's pose before or during dressing [5], [24].…”

Section: A Full Arm Dressingmentioning

confidence: 99%

Deep Haptic Model Predictive Control for Robot-Assisted Dressing

Erickson

Clever

Turk

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Robot-assisted dressing offers an opportunity to benefit the lives of many people with disabilities, such as some older adults. However, robots currently lack common sense about the physical implications of their actions on people. The physical implications of dressing are complicated by non-rigid garments, which can result in a robot indirectly applying high forces to a person's body. We present a deep recurrent model that, when given a proposed action by the robot, predicts the forces a garment will apply to a person's body. We also show that a robot can provide better dressing assistance by using this model with model predictive control. The predictions made by our model only use haptic and kinematic observations from the robot's end effector, which are readily attainable. Collecting training data from real world physical human-robot interaction can be time consuming, costly, and put people at risk. Instead, we train our predictive model using data collected in an entirely self-supervised fashion from a physics-based simulation. We evaluated our approach with a PR2 robot that attempted to pull a hospital gown onto the arms of 10 human participants. With a 0.2s prediction horizon, our controller succeeded at high rates and lowered applied force while navigating the garment around a persons fist and elbow without getting caught. Shorter prediction horizons resulted in significantly reduced performance with the sleeve catching on the participants' fists and elbows, demonstrating the value of our model's predictions. These behaviors of mitigating catches emerged from our deep predictive model and the controller objective function, which primarily penalizes high forces.

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Personalized Assistance for Dressing Users

Cited by 50 publications

References 15 publications

Iterative path optimisation for personalised dressing assistance using vision and force information

Iterative path optimisation for personalised dressing assistance using vision and force information

Active robot-assisted feeding with a general-purpose mobile manipulator: Design, evaluation, and lessons learned

Deep Haptic Model Predictive Control for Robot-Assisted Dressing

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