Embodied tactile perception and learning

Liu, Huaping; Guo, Di; Sun, Fuchun; Yang, Wuqiang; Furber, Steve; Sun, Tengchen

doi:10.26599/bsa.2020.9050012

Cited by 10 publications

(5 citation statements)

References 162 publications

(180 reference statements)

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“…From the aspect of cognitive science, QA still suffers from a lack of cognitive capacity that various experiments are required for finding exact solutions, especially for largescale cases. That is, if QA is further combined with brain-inspired computing architecture, where the brainlike cognition provides directional searching guidance for quantum computing [35] , then the new architecture is expected to maximize the potentials of quantum computing and classical computing. For example, if an improved Boolean function can be delivered to the quantum annealer as the initialized state, then QA can work well, because the initial state may be closer to the ground state than a random initialization.…”

Section: Boolean Functions Designed By Quantum Inspired Algorithmmentioning

confidence: 99%

New advanced computing architecture for cryptography design and analysis by D-Wave quantum annealer

Wang

et al. 2022

Tsinghua Sci. Technol.

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Universal quantum computers are far from achieving practical applications. The D-Wave quantum computer is initially designed for combinatorial optimizations. Therefore, exploring the potential applications of the D-Wave device in the field of cryptography is of great importance. First, although we optimize the general quantum Hamiltonian on the basis of the structure of the multiplication table (factor up to 1 005 973), this study attempts to explore the simplification of Hamiltonian derived from the binary structure of the integers to be factored. A simple factorization on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the D-Wave device. Second, by using the quantum computing cryptography based on the D-Wave 2000Q system, this research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired Simulated Annealing (QISA) framework. Good functions and a high-performance platform are introduced, and additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be found. Further comparison between QISA and Quantum Annealing (QA) on six-variable bent functions not only shows the potential speedup of QA, but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a practical cryptography design.

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Section: Boolean Functions Designed By Quantum Inspired Algorithmmentioning

confidence: 99%

New advanced computing architecture for cryptography design and analysis by D-Wave quantum annealer

Wang

et al. 2022

Tsinghua Sci. Technol.

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“…Snakes, meanwhile, can detect vibrations in the air and through the ground via their body surface, known as somatic hearing ( 54 ). In their work on tactile intelligence, Liu et al point to the need for further research in this area: “ The inherent characteristics of tactile signals have not yet been fully explored ” ( 55 ).…”

Section: Perception Aesthetic Sensibility and Behaviormentioning

confidence: 99%

Expanding Aesthetics

French

2022

Front. Vet. Sci.

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This paper seeks to expand traditional aesthetic dimensions of design beyond the limits of human capability in order to encompass other species' sensory modalities. To accomplish this, the idea of inclusivity is extended beyond human cultural and personal identities and needs, to embrace multi-species experiences of places, events and interactions in the world. This involves drawing together academic perspectives from ecology, neuroscience, anthropology, philosophy and interaction design, as well as exploring artistic perspectives and demonstrating how these different frames of reference can inspire and complement each other. This begins with a rationale for the existence of non-human aesthetics, followed by an overview of existing research into non-human aesthetic dimensions. Novel aesthetic categories are proposed and the challenge of how to include non-human aesthetic sensibility in design is discussed.

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“…[22][23][24][25] However, artificial intelligence in tactile research obviously lags behind other perception methods such as vision and hearing. [26] Here, we proposed a data-driven method based on convolutional-generative adversarial network (CGAN) to evaluate the structural parameters from a small amount of static strain response. The proposed CGAN model consisted of a well-designed generative adversarial network model (GAN) to inpaint strain information and a sophisticated convolutional neural network model (CNN) to predict structural parameters.…”

Section: Doi: 101002/aisy202100187mentioning

confidence: 99%

Convolutional‐Generative Adversarial Network: Data‐Driven Mechanical Inverse Method for Intelligent Tactile Perception

Zhang

et al. 2022

Advanced Intelligent Systems

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Reconstructing structural parameters of a system according to a known limited strain information is a typical mechanical inverse problem. [1] Among the many applications of the inverse problem, tactile perception has attracted a lot of attention from researchers recently. [2,3] Tactile perception plays a key role in physical interaction between human and the environment, and to ensure human's safety and stability. [4,5] Based on the signal of tactile perception, brain interprets it and then outputs the structural parameters of the object, such as the size, shape, texture, and hardness. [6][7][8][9][10] Comparing with other perception methods, the mechanical signal extracted by tactile perception is limited, while the sensed structural parameters of the object is complex. [11] In addition to the important application related to human, tactile perception also plays a critical role in robot interactions. [12][13][14][15] As shown in Figure 1, a conceptual robot of tactile perception consists of sensing and processing systems. Tactile sensors including strain gauges and data acquisition board are integrated into the fingers, which are used to evaluate the features of the target object, such as shape and structural parameters. Researches have shown that the state of the system can be evaluated by limited strain response. [16,17] However, when the features of the target object get more complex, limited strain response will bring difficulties to prediction. What is worse, in the long-term use of the tactile system, possible damage of strain sensors will lead to further loss of strain information. [18,19] The limited strain input results in significantly decreasing the accuracy rate of predicting structural parameters. Therefore, to create robots to perceive, explore, and manipulate their environments, an algorithm to process the tactile information like human needed to be developed, which is also the key factor that limits the development of robotic tactile perception.For the tactile perception, it would be much more difficult to solve the mechanical inverse problem by utilizing the aforesaid classical methods, such as Tikhonov regularization [20] and TV regularization. [21] The difficulties originate from two aspects. On the one hand, the strain field of structure needs to satisfy the mechanical governing equations and boundary conditions, which is intricate owing to the complexity of the loading and structure under real working conditions. Besides, the shape and pattern of microstructures severely affect the localized spatial relations of the strain data. Therefore, we need to consider both the global and local relations of the mechanical field when predicting structures. On the other hand, the predicted structure contains numerous randomly distributed microstructures, which will engender large categories while using the traditional classification algorithm. Generally, the problem of low prediction accuracy arises when the strain data measured are limited and the categories need to be distinguished are diverse. To ove...

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Embodied tactile perception and learning

Cited by 10 publications

References 162 publications

New advanced computing architecture for cryptography design and analysis by D-Wave quantum annealer

New advanced computing architecture for cryptography design and analysis by D-Wave quantum annealer

Expanding Aesthetics

Convolutional‐Generative Adversarial Network: Data‐Driven Mechanical Inverse Method for Intelligent Tactile Perception

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