A Data-driven Planning Framework for Robotic Texture Painting on 3D Surfaces

Abstract

Painting textures on 3D surfaces requires an understanding of the surface geometry, paint flow and paint mixing. This work formulates automated painting as a planning problem and proposes a solution based on a self-supervised learning framework that enables a robot to paint monochromatic non-uniform textures on 3D surfaces. We developed a method that iteratively decides the actions to take based on constant feedback of the painting process. Inspired by recent results, we formulate our solution using a recurrent neural network (RNN) to decide where and what to paint on the surface at each time instant. Specifically, the paint delivery tool's flow rate, orientation and position relative to the surface at each time instant are evaluated. This data can then be processed by a robot's planner of choice for generating a painting mission that can achieve the desired end result. We evaluate the proposed approach by providing qualitative and quantitative results of the different components. Furthermore, we validate the effectiveness of the approach for the application by providing renderings from a paint simulation environment and show how a robot executes the planned painting mission on a generic 3D surface.