Action Augmentation of Tactile Perception for Soft-Body Palpation

Scimeca, Luca; Hughes, Josie; Maiolino, Perla; He, Liang; Nanayakkara, Thrishantha; Iida, Fumiya

doi:10.1089/soro.2020.0129

Cited by 26 publications

(17 citation statements)

References 49 publications

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“…Control of sensor response can allow us to better discriminate between different stimuli, whether from control of the hand-environment interactions to amplify the desired response [74], or by actively controlling the morphology to sense different properties [75]. In summary, exploiting a passive soft body for sensing and perception offers perhaps the biggest challenge in the passive hand framework and is the area least explored.…”

Section: Passive Dynamic Sensor Interfacementioning

confidence: 99%

Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design

2023

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Robotic hands have long strived to reach the performance of human hands. The physical complexity and extraordinary capabilities of the human hand, in terms of sensing, actuation and cognitive abilities, makes achieving this goal challenging. At the heart of the physical structure of the hand is its' passive behaviours. Seen most clearly in soft robotic hands, these behaviours influence and affect the mechanical, sensing and control functionalities. With this perspective, we present a framework through which passivity in robot hands can be understood, by concretely identifying the role of passivity in the design, fabrication and control of soft hands. In this framework we focus on the interactions between the physical hand and the: environment, internal actuation, sensor morphology and wrist control. Taking these surrounding systems away, we are left with a passive soft hand whose behaviours emerge from external interactions. Inspired by the human hand, we define the role of these four key interacting pillars and review how state-of-the art robot hands utilize these four elements to aid functionality. We show how these pillars promote hybrid soft-rigid hands with rich behaviours, providing benefits in terms of the increased adaptability to uncertain environments, improved scalability and reduction in the cost of actuation, sensing and control. This review provides a conceptual framework for approaching hand design and analysis through consideration of the passive behaviours. This highlights not only the advances that can be made by approaching the problem in this way, but also the outstanding challenges that stem from this outlook.

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Section: Passive Dynamic Sensor Interfacementioning

confidence: 99%

Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design

2023

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“…Actuation and perception can be considered as an integration. 10,11 When an object interacts with the environment, the tactile measurements ultimately depend on the sensor configurations, especially in soft sensors. In other words, the physical property of the sensor/agent acts as a physical reservoir that filters the tactile information for active sensing.…”

Section: Introductionmentioning

confidence: 99%

“…14 Incorporating actuation in perception shows a new trend to develop robotic counterparts to understand the environment more effectively. 11 Another example can be found in active haptic exploration to localize a nodule in soft tissue with a tunable-stiffness robotic probe. 15 Examining the tissue with different stiffness of the probe can significantly reduce the uncertainty of the measured haptic information, thus, more effective in the detection.…”

Section: Introductionmentioning

confidence: 99%

3D-Printed Soft Sensors for Adaptive Sensing with Online and Offline Tunable Stiffness

Herzig

Nanayakkara

et al. 2022

Soft Robotics

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The stiffness of a soft robot with structural cavities can be regulated by controlling the pressure of a fluid to render predictable changes in mechanical properties. When the soft robot interacts with the environment, the mediating fluid can also be considered an inherent information pathway for sensing. This approach to using structural tuning to improve the efficacy of a sensing task with specific states has not yet been well studied. A tunable stiffness soft sensor also renders task-relevant contact dynamics in soft robotic manipulation tasks. This article proposes a type of adaptive soft sensor that can be directly three-dimensional printed and controlled using pneumatic pressure. The tunability of such a sensor helps to adjust the sensing characteristics to better capturing specific tactile features, demonstrated by detecting texture with different frequencies. We present the design, modeling, Finite Element Simulation, and experimental characterization of a single unit of such a tunable stiffness sensor. How the sensing characteristics are affected by adjusting its stiffness is studied in depth. In addition to the tunability, the results show that such types of adaptive sensors exhibit good sensitivity (up to 2.6 KPa/N), high sensor repeatability (average std <0.008 KPa/N), low hysteresis (<6%), and good manufacturing repeatability (average std = 0.0662 KPa/N).

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“…Previous studies in the field of filter-based tactile sensing have focused on the effect of the filter’s morphology on the structure and quality of the recorded data, investigating the information gain’s maximization ( Thuruthel et al, 2020 ), the amplification of the sensor’s sensitivity ( Fend et al, 2004 ), task-specific optimization ( Qi and Hirai, 2019 ), and the relationship between redundancy and localization error ( Costi et al, 2021b ). Moreover, researchers have deeply studied the relationship between the morphology of the sensor and the action for perception ( Huang et al, 2019 ; Scimeca et al, 2020b , 2021 ). From their results, it emerges how in action–based perception is strongly affected by the morphology of the tactile filter ( Bernth et al, 2018 ) and the inevitable trade-off when selecting the material of the filter.…”

Section: Introductionmentioning

confidence: 99%

How the Environment Shapes Tactile Sensing: Understanding the Relationship Between Tactile Filters and Surrounding Environment

2022

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The mechanical properties of a sensor strongly affect its tactile sensing capabilities. By exploiting tactile filters, mechanical structures between the sensing unit and the environment, it is possible to tune the interaction dynamics with the surrounding environment. But how can we design a good tactile filter? Previously, the role of filters’ geometry and stiffness on the quality of the tactile data has been the subject of several studies, both implementing static filters and adaptable filters. State-of-the-art works on online adaptive stiffness highlight a crucial role of the filters’ mechanical behavior in the structure of the recorded tactile data. However, the relationship between the filter’s and the environment’s characteristics is still largely unknown. We want to show the effect of the environment’s mechanical properties on the structure of the acquired tactile data and the performance of a classification task while testing a wide range of static tactile filters. Moreover, we fabricated the filters using four materials commonly exploited in soft robotics, to merge the gap between tactile sensing and robotic applications. We collected data from the interaction with a standard set of twelve objects of different materials, shapes, and textures, and we analyzed the effect of the filter’s material on the structure of such data and the performance of nine common machine learning classifiers, both considering the overall test set and the three individual subsets made by all objects of the same material. We showed that depending on the material of the test objects, there is a drastic change in the performance of the four tested filters, and that the filter that matches the mechanical properties of the environment always outperforms the others.

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Action Augmentation of Tactile Perception for Soft-Body Palpation

Cited by 26 publications

References 49 publications

Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design

Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design

3D-Printed Soft Sensors for Adaptive Sensing with Online and Offline Tunable Stiffness

How the Environment Shapes Tactile Sensing: Understanding the Relationship Between Tactile Filters and Surrounding Environment

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