A Comprehensive Realization of Robot Skin: Sensors, Sensing, Control, and Applications

Cheng, Gordon; Dean-León, Emmanuel; Bergner, Florian; Guadarrama-Olvera, Julio Rogelio; Leboutet, Quentin; Mittendorfer, Philipp

doi:10.1109/jproc.2019.2933348

Cited by 133 publications

(90 citation statements)

References 82 publications

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“…The main challenges in whole-body sensing are the organization and calibration of a large number of spatially distributed multi-modal tactile sensing elements (113). Spatial calibration can be manually performed or automated using robot kinematics and action inference techniques (114).…”

Section: Reactionmentioning

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: Reactionmentioning

confidence: 99%

Electronic skins and machine learning for intelligent soft robots

et al. 2020

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“…The AIs that we are currently building, and will continue to build, are quite different from us in terms of their function and constitution. Consider, for example, H-1-an autonomous humanoid robot developed by Gordon Cheng and his team at Technical University of Munich (Cheng et al 2019). While similar to other humanoid robots, e.g., Boston Dynamic's Atlas or Honda's ASIMO, H-1 is unique in that it is equipped with artificial robot skin.…”

Section: Epistemological Problems With Identifying Functional Analoguesmentioning

confidence: 99%

The hard problem of AI rights

Andreotta

2020

AI & Soc

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In the past few years, the subject of AI rights-the thesis that AIs, robots, and other artefacts (hereafter, simply 'AIs') ought to be included in the sphere of moral concern-has started to receive serious attention from scholars. In this paper, I argue that the AI rights research program is beset by an epistemic problem that threatens to impede its progress-namely, a lack of a solution to the 'Hard Problem' of consciousness: the problem of explaining why certain brain states give rise to experience. To motivate this claim, I consider three ways in which to ground AI rights-namely: superintelligence, empathy, and a capacity for consciousness. I argue that appeals to superintelligence and empathy are problematic, and that consciousness should be our central focus, as in the case of animal rights. However, I also argue that AI rights is disanalogous from animal rights in an important respect: animal rights can proceed without a solution to the 'Hard Problem' of consciousness. Not so with AI rights, I argue. There we cannot make the same kinds of assumptions that we do about animal consciousness, since we still do not understand why brain states give rise to conscious mental states in humans.

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“…Different works have addressed the tactile data classification problem, using different methods including, but not limited to, machine learning and deep learning [6][7][8][9][10]. While most of the work done was focused on the methodology itself, few works addressed the implementation on embedded platforms where the real application should reside.…”

Section: State-of-the-artmentioning

confidence: 99%

Smart Tactile Sensing Systems Based on Embedded CNN Implementations

et al. 2020

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Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent. This paper presents and compares the implementations of a convolutional neural network model for tactile data decoding on various hardware platforms. Experimental results show comparable classification accuracy of 90.88% for Model 3, overcoming similar state-of-the-art solutions in terms of time inference. The proposed implementation achieves a time inference of 1.2 ms while consuming around 900 μ J. Such an embedded implementation of intelligent tactile data decoding algorithms enables tactile sensing systems in different application domains such as robotics and prosthetic devices.

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A Comprehensive Realization of Robot Skin: Sensors, Sensing, Control, and Applications

Abstract: This article presents a holistic approach to engineer the artificial skin for robots with an example of a multimodal skin cell showing multiple humanlike sensing modalities.

Cited by 133 publications

References 82 publications

Electronic skins and machine learning for intelligent soft robots

Electronic skins and machine learning for intelligent soft robots

The hard problem of AI rights

Smart Tactile Sensing Systems Based on Embedded CNN Implementations

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