Design and fabrication of a soft robotic hand and arm system

Alspach, Alexander; Kim, Joohyung; Yamane, Katsu

doi:10.1109/robosoft.2018.8404947

Cited by 33 publications

(16 citation statements)

References 10 publications

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“…Currently, the emerging trend in reduction of mechatronic complexity and degrees of actuation is generating solutions with high grasping performance, but, apparently, reduced capabilities in term of dexterity. Such situations are rapidly changing thanks to the introduction of novel control paradigms (for more details see section 3.3) or design solutions were a balance between complexity and dexterity is achieved trough the combination of different technologies (Tincani et al, 2013 ; Spiers et al, 2018 ) or actuation architectures (Alspach et al, 2018 ; Della Santina et al, 2018 ).…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

“…Integration of sensors for detecting contacts on arms and palms is relevant (Gardecki and Podpora, 2017 ). In the research literature, there are some examples of specially developed sensors for touch Schmitz et al ( 2010 ) and hugs (Alspach et al, 2018 ). The overall acceptance of social robotic systems is strongly dependent on the cultural background of people and nations and these differences should be taken into account (Lee et al, 2016 ).…”

Section: Hands In the Fieldmentioning

confidence: 99%

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Hands in the Real World

2020

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Robots face a rapidly expanding range of potential applications beyond controlled environments, from remote exploration and search-and-rescue to household assistance and agriculture. The focus of physical interaction is typically delegated to end-effectors-fixtures, grippers or hands-as these machines perform manual tasks. Yet, effective deployment of versatile robot hands in the real world is still limited to few examples, despite decades of dedicated research. In this paper we review hands that found application in the field, aiming to discuss open challenges with more articulated designs, discussing novel trends and perspectives. We hope to encourage swift development of capable robotic hands for long-term use in varied real world settings. The first part of the paper centers around progress in artificial hand design, identifying key functions for a variety of environments. The final part focuses on the overall trends in hand mechanics, sensors and control, and how performance and resiliency are qualified for real world deployment.

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Section: Discussion and Perspectivesmentioning

confidence: 99%

Section: Hands In the Fieldmentioning

confidence: 99%

Hands in the Real World

2020

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“…Many of these sensors contain conductive and stretchable materials to produce resistive or capacitive strain sensors (2,16,51,52). Other groups used optical devices like cameras and optical fibers to sense deformations within an actuator (53)(54)(55). Several of these existing sensors are well-suited for measuring characteristics like strain, pressure, and bending but do not enable the high sensor densities or resolutions that have been demonstrated in e-skins.…”

Section: Skin-based Sensing For Soft Robotsmentioning

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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“…Soft and inflatable robots have garnered increasing attention as they can provide more adaptive and resilient movement, as well as a safer, more compliant interface with the environment. Soft and inflatable robots also offer new design opportunities for human-robot interaction [3,32] and, given their soft exterior as affordances, are particularly suited for touch-based interaction. That said, the use of force sensors for tactile interaction makes this design choice infeasible, as soft robots generally undergo skin deformation when they are actuated and usually do not have a rigid skin even after inflation.…”

Section: Shadowsensementioning

confidence: 99%

ShadowSense

Bejarano

Hoffman

2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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This paper proposes and evaluates the use of image classification for detailed, full-body human-robot tactile interaction. A camera positioned below a translucent robot skin captures shadows generated from human touch and infers social gestures from the captured images. This approach enables rich tactile interaction with robots without the need for the sensor arrays used in traditional social robot tactile skins. It also supports the use of touch interaction with non-rigid robots, achieves high-resolution sensing for robots with different sizes and shape of surfaces, and removes the requirement of direct contact with the robot. We demonstrate the idea with an inflatable robot and a standing-alone testing device, an algorithm for recognizing touch gestures from shadows that uses Densely Connected Convolutional Networks, and an algorithm for tracking positions of touch and hovering shadows. Our experiments show that the system can distinguish between six touch gestures under three lighting conditions with 87.5 - 96.0% accuracy, depending on the lighting, and can accurately track touch positions as well as infer motion activities in realistic interaction conditions. Additional applications for this method include interactive screens on inflatable robots and privacy-maintaining robots for the home.

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Design and fabrication of a soft robotic hand and arm system

Cited by 33 publications

References 10 publications

Hands in the Real World

Hands in the Real World

Electronic skins and machine learning for intelligent soft robots

ShadowSense

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