Tactile sensing plays an important role in robotic perception and manipulation tasks. To overcome the real-world limitations of data collection, simulating tactile response in a virtual environment comes as a desirable direction of robotic research. In this paper, we propose Elastic Interaction of Particles (EIP) for tactile simulation. Most existing works model the tactile sensor as a rigid multi-body, which is incapable of reflecting the elastic property of the tactile sensor as well as characterizing the fine-grained physical interaction between the two objects. By contrast, EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. With the tactile simulation by EIP, we further propose a tactile-visual perception network that enables information fusion between tactile data and visual images. The perception network is based on a global-to-local fusion mechanism where multi-scale tactile features are aggregated to the corresponding local region of the visual modality with the guidance of tactile positions and directions. The fusion method exhibits superiority regarding the 3D geometric reconstruction task.
翻译:触觉感测在机器人感知和操控任务中起着重要作用。 要克服数据收集中真实世界的局限性, 模拟虚拟环境中的触觉反应是机器人研究的可取方向。 在本文中, 我们提议对触摸模拟采用粒子的等量互动( EIP ) 。 大多数现有作品将触觉感应器建模为硬性多体, 无法反映触觉感官的弹性特性, 也无法描述两个对象之间的细微物理互动。 相反, EIP 模型将触觉感应器建模为一组协调粒子, 而弹性属性则用于调节接触期间粒子的变形。 在EIP 的触摸模拟中, 我们进一步提议一个触觉- 视觉感知网络, 使触觉数据与视觉图像之间能够信息融合。 感知网络以全球到本地的感知机制为基础, 在那里, 多尺度触觉特征可聚合特性可聚合到相应的视觉模式区域, 并指导地触觉定位定位定位位置和方向 。 度图象性图象学图象学 3 。