Neural learning of the topographic tactile sensory information of an artificial skin through a self-organizing map

Pugach, Ganna; Pitti, Alexandre; Gaussier, Philippe

doi:10.1080/01691864.2015.1092395

Cited by 13 publications

(9 citation statements)

References 57 publications

(55 reference statements)

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“…1(b). Its implementation is explained in refs 48, 49, 50. The voltage of the electrical current injected into each pair of electrodes is read out, and the potential distribution on the global surface of the sensor sheet is estimated based on the inverse analysis of the local resistance in each pair, called electrical impedance tomography (EIT).…”

Section: Devices and Methodsmentioning

confidence: 99%

Spatio-Temporal Tolerance of Visuo-Tactile Illusions in Artificial Skin by Recurrent Neural Network with Spike-Timing-Dependent Plasticity

Pitti¹,

Pugach²,

Gaussier³

et al. 2017

Sci Rep

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Perceptual illusions across multiple modalities, such as the rubber-hand illusion, show how dynamic the brain is at adapting its body image and at determining what is part of it (the self) and what is not (others). Several research studies showed that redundancy and contingency among sensory signals are essential for perception of the illusion and that a lag of 200–300 ms is the critical limit of the brain to represent one’s own body. In an experimental setup with an artificial skin, we replicate the visuo-tactile illusion within artificial neural networks. Our model is composed of an associative map and a recurrent map of spiking neurons that learn to predict the contingent activity across the visuo-tactile signals. Depending on the temporal delay incidentally added between the visuo-tactile signals or the spatial distance of two distinct stimuli, the two maps detect contingency differently. Spiking neurons organized into complex networks and synchrony detection at different temporal interval can well explain multisensory integration regarding self-body.

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Section: Devices and Methodsmentioning

confidence: 99%

Spatio-Temporal Tolerance of Visuo-Tactile Illusions in Artificial Skin by Recurrent Neural Network with Spike-Timing-Dependent Plasticity

Pitti¹,

Pugach²,

Gaussier³

et al. 2017

Sci Rep

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“…6 the activity of the SOM after learning (right column) and for five different locations around the arm (left column). As observed in a previous work [23], the Kohonen map learns the topological configuration of the tactile sheet without giving its XY coordinates. The resolution here is just 16×16 although, it is possible to go higher.…”

Section: B Experiments 2 -Learning Stage On Tactile Neural Network Sementioning

confidence: 80%

“…The model is applied to a two degrees-of-freedom manipulator but can be easily extended to a humanoid's full body. At first, the voltage signals are learned through a Kohonen SOM that recreates the topology of the tactile sheet [23]. In second, this information is then used by four LMS neurons that simulate a torque vector in the four directions at the contact point.…”

Section: Discussionmentioning

confidence: 99%

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Touch-based admittance control of a robotic arm using neural learning of an artificial skin

Pugach

Melnyk

Tolochko

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Touch perception is an important sense to model in humanoid robots to interact physically and socially with humans. We present a neural controller that can adapt the compliance of the robot arm in four directions using as input the tactile information from an artificial skin and as output the estimated torque for admittance control-loop reference. This adaption is done in a self-organized fashion with a neural system that learns first the topology of the tactile map when we touch it and associates a torque vector to move the arm in the corresponding direction. The artificial skin is based on a large area piezoresistive tactile device (ungridded) that changes its electrical properties in the presence of the contact. Our results show the self-calibration of a robotic arm (2 degrees of freedom) controlled in the four directions and derived combination vectors, by the soft touch on all the tactile surface, even when the torque is not detectable (force applied near the joint). The neural system associates each tactile receptive field with one direction and the correct force. We show that the tactile-motor learning gives better interactive experiments than the admittance control of the robotic arm only. Our method can be used in the future for humanoid adaptive interaction with a human partner.

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“…[70,71], which can adapt to the elbow of a robot with 19 electrodes and was designed into a thin, flexible, and stretchable artificial skin that can be applied on the fore arm and upper arm of the robot to obtain information (e.g., contact position, contact duration, and contact intensity). Furthermore, Pugach et al [72] and Park et al [73] used neural networks to implement image reconstruction. Fig.…”

Section: Advanced Tactile Sensing For Jointsmentioning

confidence: 99%

Embodied tactile perception and learning

Liu

Guo

Sun

et al. 2020

Brain Science Advances

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Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

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Neural learning of the topographic tactile sensory information of an artificial skin through a self-organizing map

Cited by 13 publications

References 57 publications

Spatio-Temporal Tolerance of Visuo-Tactile Illusions in Artificial Skin by Recurrent Neural Network with Spike-Timing-Dependent Plasticity

Spatio-Temporal Tolerance of Visuo-Tactile Illusions in Artificial Skin by Recurrent Neural Network with Spike-Timing-Dependent Plasticity

Touch-based admittance control of a robotic arm using neural learning of an artificial skin

Embodied tactile perception and learning

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