Embodied tactile perception and learning

Huaping Liu, Di Guo, F. Sun, Wuqiang Yang, S. Furber, Teng Sun
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引用次数: 4

Abstract

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.
体现触觉感知和学习
各种生物都表现出体现智能,这是通过大脑、身体和环境的协同相互作用来反映的。实施体智能的实际行为是主体与环境通过信息感知和物理操纵进行持续的动态交互而产生的。机器人与环境之间的物理交互是实现具身感知和学习的基础。触觉信息在这种物理交互过程中起着至关重要的作用。它可以用于确保安全性、稳定性和依从性,并且可以提供使用其他感知模式难以捕获的独特信息。然而,由于现有传感器以及感知和学习方法的限制,机器人触觉研究的发展明显滞后于视觉和听觉等其他感知方式,严重制约了机器人实施体智能的发展。介绍了目前机器人触觉体现智能研究面临的挑战,综述了机器人触觉体现智能的理论和方法。基于新型大规模触觉阵列传感装置的发展,可以设计触觉感知和学习方法,以突破触觉智能的神经形态计算技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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