A framework for robotic clothing assistance by imitation learning

Joshi, Ravi Prakash; Koganti, Nishanth; Shibata, Tomohiro

doi:10.1080/01691864.2019.1636715

Cited by 30 publications

(14 citation statements)

References 9 publications

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“…Similarly, modeling of human motion uncertainty has only been performed for collaborative tasks. The works of Yamazaki et al (2014) , Gao et al (2015) , Yu et al (2017) , Joshi et al (2019) and Koganti et al (2019) model this uncertainty in close-proximity collaborative tasks in specific scenarios in which either a global trajectory is learned or motor skills are encoded. However, the human movement modeled in these studies are for general skills to perform a task without any considerations of disrupted human movement.…”

Section: Related Workmentioning

confidence: 99%

“…This methodology takes advantage of visualization to spot disruption within the human movement as a change in the collaborative behavior. Latent variable models ( Orrite-Urunuela et al, 2004 ) are used to address these challenges ( Zhang et al, 2017 ; Joshi et al, 2019 ) to model limitations in human movement or to personalize human movements. This paper, will use a related approach to model the human movement and highlight any disruptions associated with the change in collaborative behavior.…”

Section: Related Workmentioning

confidence: 99%

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A Study on the Effects of Cognitive Overloading and Distractions on Human Movement During Robot-Assisted Dressing

2022

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For robots that can provide physical assistance, maintaining synchronicity of the robot and human movement is a precursor for interaction safety. Existing research on collaborative HRI does not consider how synchronicity can be affected if humans are subjected to cognitive overloading and distractions during close physical interaction. Cognitive neuroscience has shown that unexpected events during interactions not only affect action cognition but also human motor control Gentsch et al. (Cognition, 2016, 146, 81–89). If the robot is to safely adapt its trajectory to distracted human motion, quantitative changes in the human movement should be evaluated. The main contribution of this study is the analysis and quantification of disrupted human movement during a physical collaborative task that involves robot-assisted dressing. Quantifying disrupted movement is the first step in maintaining the synchronicity of the human-robot interaction. The human movement data collected from a series of experiments where participants are subjected to cognitive loading and distractions during the human-robot interaction, are projected in a 2-D latent space that efficiently represents the high-dimensionality and non-linearity of the data. The quantitative data analysis is supported by a qualitative study of user experience, using the NASA Task Load Index to measure perceived workload, and the PeRDITA questionnaire to represent the human psychological state during these interactions. In addition, we present an experimental methodology to collect interaction data in this type of human-robot collaboration that provides realism, experimental rigour and high fidelity of the human-robot interaction in the scenarios.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Study on the Effects of Cognitive Overloading and Distractions on Human Movement During Robot-Assisted Dressing

2022

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“…Besides normal gait, DMPs were also applied for stairascend (Xu et al 2020) and sit-to-stand (Kamali et al 2016) assistive movements of lower-body exoskeletons. In (Joshi et al 2019), a robotic arm was used to assist humans with putting the cloths on their body, where the movements were generated by DMPs.…”

Section: Human Assistance Augmentation and Rehabilitationmentioning

confidence: 99%

Dynamic Movement Primitives in Robotics: A Tutorial Survey

Saveriano¹,

Abu‐Dakka²,

Kramberger³

et al. 2021

Preprint

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Biological systems, including human beings, have the innate ability to perform complex tasks in versatile and agile manner. Researchers in sensorimotor control have tried to understand and formally define this innate property. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally lead to the formulation of the motor primitives theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems, and are well suited to generate motor commands for artificial systems like robots. In the last decades, Dynamic Movement Primitives (DMPs) have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human-robot co-working, resulting a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMPs formulations in rigorous mathematical terms, and discuss advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state of the art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions.

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“…One common pose estimation method during physical interaction is using visual features from an RGB or depth camera. For example, several works have used depth sensing for estimating human pose [68], [69], [70], [71] and tracking cloth features [72], [73], [74] while a robot helps a person or mannequin dress on a clothing garment. Jiménez et al [75] provide an overview of various perception techniques for tracking cloth during assistive robotic tasks.…”

Section: Human Pose Estimation For Physical Assistancementioning

confidence: 99%

Characterizing Multidimensional Capacitive Servoing for Physical Human-Robot Interaction

Erickson¹,

Clever²,

Gangaram³

et al. 2021

Preprint

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Towards the goal of robots performing robust and intelligent physical interactions with people, it is crucial that robots are able to accurately sense the human body, follow trajectories around the body, and track human motion. This study introduces a capacitive servoing control scheme that allows a robot to sense and navigate around human limbs during close physical interactions. Capacitive servoing leverages temporal measurements from a multi-electrode capacitive sensor array mounted on a robot's end effector to estimate the relative position and orientation (pose) of a nearby human limb. Capacitive servoing then uses these human pose estimates from a datadriven pose estimator within a feedback control loop in order to maneuver the robot's end effector around the surface of a human limb. We provide a design overview of capacitive sensors for human-robot interaction and then investigate the performance and generalization of capacitive servoing through an experiment with 12 human participants. The results indicate that multidimensional capacitive servoing enables a robot's end effector to move proximally or distally along human limbs while adapting to human pose. Using a cross-validation experiment, results further show that capacitive servoing generalizes well across people with different body size.

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A framework for robotic clothing assistance by imitation learning

Cited by 30 publications

References 9 publications

A Study on the Effects of Cognitive Overloading and Distractions on Human Movement During Robot-Assisted Dressing

A Study on the Effects of Cognitive Overloading and Distractions on Human Movement During Robot-Assisted Dressing

Dynamic Movement Primitives in Robotics: A Tutorial Survey

Characterizing Multidimensional Capacitive Servoing for Physical Human-Robot Interaction

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