How Cognitive Models of Human Body Experience Might Push Robotics
How Cognitive Models of Human Body Experience Might Push Robotics
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DOI：
10.3389/fnbot.2019.00014
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发表时间：
2019-04-11
影响因子：
3.1
通讯作者：
Beckerle, Philipp
中科院分区：
文献类型：
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作者：
Schuermann, Tim;Mohler, Betty Jo;Beckerle, Philipp
在过去的几十年里，人类多感觉整合的认知模型已经被开发并应用于模拟人体体验。最近的研究表明，贝叶斯和连接主义模型可能会推动机器人技术各个分支的发展：辅助机器人设备可能会适应其人类用户，旨在增加设备的具体化，例如，在假肢中，人形机器人可以被赋予关于其周围空间的类似人类的能力，例如，通过与其他代理人保持安全或社会适当的距离。在这篇观点论文中，我们回顾了旨在近似人类感觉运动行为生成过程的认知模型，讨论了它们在机器人技术中的挑战和潜力，并概述了现有的方法。虽然模型的准确性仍有待提高，但人类启发的认知模型支持对人体体验的调节因素如何混合的理解。在自适应和学习控制算法中实现由此产生的见解可以帮助泰勒辅助设备适应用户的个人身体体验。开发自己身体图式的类人机器人可以考虑控制这种身体知识，并学会优化与人类及其环境的物理交互。认知身体体验模型应该在准确性和在线能力方面得到改进，以实现这些雄心勃勃的目标，这将在机器人的各个领域促进以人为本的方向。
In the last decades, cognitive models of multisensory integration in human beings have been developed and applied to model human body experience. Recent research indicates that Bayesian and connectionist models might push developments in various branches of robotics: assistive robotic devices might adapt to their human users aiming at increased device embodiment, e.g., in prosthetics, and humanoid robots could be endowed with human-like capabilities regarding their surrounding space, e.g., by keeping safe or socially appropriate distances to other agents. In this perspective paper, we review cognitive models that aim to approximate the process of human sensorimotor behavior generation, discuss their challenges and potentials in robotics, and give an overview of existing approaches. While model accuracy is still subject to improvement, human-inspired cognitive models support the understanding of how the modulating factors of human body experience are blended. Implementing the resulting insights in adaptive and learning control algorithms could help to taylor assistive devices to their user's individual body experience. Humanoid robots who develop their own body schema could consider this body knowledge in control and learn to optimize their physical interaction with humans and their environment. Cognitive body experience models should be improved in accuracy and online capabilities to achieve these ambitious goals, which would foster human-centered directions in various fields of robotics.