论文标题
多模型传感器融合用于学习富含软机器人的丰富模型
Multimodel Sensor Fusion for Learning Rich Models for Interacting Soft Robots
论文作者
论文摘要
软机器人通常被视为低维系统，尤其是在使用基于学习的方法时。这导致模型的能力有限，无法预测软机器人可以拥有的大量变形模式和相互作用。在这项工作中，我们提出了一种深入学习方法，以学习结合多模式感觉运动信息的软机器人的高维视觉模型。这些模型是以端到端方式学习的，因此不需要中间传感器处理或数据接地。这种建模方法的功能和优势显示在带有嵌入式软传感器的软拟人手指上。我们还表明，如何扩展这种方法以发展更高级别的认知功能，例如对自我的识别和外部环境以及获取对象操纵技能。这项工作是迈向整合软机器人技术和发展机器人架构的一步，以创建下一代的智能软机器人。
Soft robots are typically approximated as low-dimensional systems, especially when learning-based methods are used. This leads to models that are limited in their capability to predict the large number of deformation modes and interactions that a soft robot can have. In this work, we present a deep-learning methodology to learn high-dimensional visual models of a soft robot combining multimodal sensorimotor information. The models are learned in an end-to-end fashion, thereby requiring no intermediate sensor processing or grounding of data. The capabilities and advantages of such a modelling approach are shown on a soft anthropomorphic finger with embedded soft sensors. We also show that how such an approach can be extended to develop higher level cognitive functions like identification of the self and the external environment and acquiring object manipulation skills. This work is a step towards the integration of soft robotics and developmental robotics architectures to create the next generation of intelligent soft robots.
