Data-Driven Super-Resolution on a Tactile Dome

Piacenza, Pedro; Sherman, Sydney A.; Ciocarlie, Matei

doi:10.1109/lra.2018.2800081

Cited by 31 publications

(17 citation statements)

References 18 publications

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“…3). For example, Piacenza et al obtained high-resolution data from a robotic fingertip and used ridge regression to process this data to estimate the locations of indentations (62). Similarly, Larson et al used convolutional neural networks to learn deformations on a sensor array that can interpret human touch in soft interfaces (63).…”

Section: Machine Learning For Soft E-skinsmentioning

confidence: 99%

Electronic skins and machine learning for intelligent soft robots

et al. 2020

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Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

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Section: Machine Learning For Soft E-skinsmentioning

confidence: 99%

Electronic skins and machine learning for intelligent soft robots

et al. 2020

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“…[ 136 ] In addition to optical methods, magnetic sensors are also suitable for high‐resolution pressure perception. [ 143 ] A pressure sensor was recently developed using soft materials that contain magnetic particles, where the external force applied changes the uniformly distributed magnetic particles and changes the internal magnetic field in the device. [ 99 ] As another example, Figure 4M,N illustrates a GMI‐based pressure sensor with a high sensitivity to force stimuli.…”

Section: Sensingmentioning

confidence: 99%

Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics

2022

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Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human-machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human-machine interfaces on soft robotics would make a promising alternative to conventional rigid devices, which could potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible humanmachine interfaces are summarized by recent and renown applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human-machine interface in medical robotics.

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“…Different approaches have been proposed for mapping raw tactile pressure values to the contact forces and torques [73]- [75]. One example is to model the mapping as a linear function [76], [77].…”

Section: B Force and Torquementioning

confidence: 99%

A Review of Tactile Information: Perception and Action Through Touch

Kroemer

et al. 2020

IEEE Trans. Robot.

200

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Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from contacts between the robot and its environment. The data is however not limited to individual contacts and can be used to extract a wide range of information about the objects in the robots environment as well as the robots own actions during the interactions.In this paper, we provide an overview of tactile information and its applications in robotics. We present a hierarchy consisting of raw, contact, object, and action levels to structure the tactile information, with higher-level information often building upon lower-level information. We discuss different types of information that can be extracted at each level of the hierarchy. The paper also includes an overview of different types of robot applications and the types of tactile information that they employ.The paper concludes with a discussion of tactile-based computational framework and future tactile applications which are still beyond current robot's capabilities.

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Data-Driven Super-Resolution on a Tactile Dome

Cited by 31 publications

References 18 publications

Electronic skins and machine learning for intelligent soft robots

Electronic skins and machine learning for intelligent soft robots

Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics

A Review of Tactile Information: Perception and Action Through Touch

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