Computational Tactile Flow for Anthropomorphic Grippers
This work addresses the challenge of integrating tactile perception for anthropomorphic grippers, offering a domain-specific incremental improvement in robotic manipulation.
The paper tackled the problem of analyzing tactile feedback in isolation for robotic grasping by introducing computational tactile flow, which improved the extraction of fine-grained surface features and provided information on motion direction and 3D structure.
Grasping objects requires tight integration between visual and tactile feedback. However, there is an inherent difference in the scale at which both these input modalities operate. It is thus necessary to be able to analyze tactile feedback in isolation in order to gain information about the surface the end-effector is operating on, such that more fine-grained features may be extracted from the surroundings. For tactile perception of the robot, inspired by the concept of the tactile flow in humans, we present the computational tactile flow to improve the analysis of the tactile feedback in robots using a Shadow Dexterous Hand. In the computational tactile flow model, given a sequence of pressure values from the tactile sensors, we define a virtual surface for the pressure values and define the tactile flow as the optical flow of this surface. We provide case studies that demonstrate how the computational tactile flow maps reveal information on the direction of motion and 3D structure of the surface, and feedback regarding the action being performed by the robot.