Elastic Tactile Simulation Towards Tactile-Visual Perception
This addresses the challenge of tactile data collection for robotic perception and manipulation, offering a simulation-based solution with potential applications in robotics, though it appears incremental as it builds on existing tactile simulation methods.
The paper tackled the problem of simulating tactile response in robotics by proposing Elastic Interaction of Particles (EIP) to model elastic properties and fine-grained interactions, and introduced a tactile-visual perception network that improved 3D geometric reconstruction with demonstrated superiority.
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.