Efficient deformable registration of multi-resolution surfel maps for object manipulation skill transfer

Endowing mobile manipulation robots with skills to use objects and tools often involves the programming or training on specific object instances. To apply this knowledge to novel instances from the same class of objects, a robot requires generalization capabilities for control as well as perception. In this paper, we propose an efficient approach to deformable registration of RGB-D images that enables robots to transfer skills between object instances. Our method provides a dense deformation field between the current image and an object model which allows for estimating local rigid transformations on the object´s surface. Since we define grasp and motion strategies as poses and trajectories with respect to the object models, these strategies can be transferred to novel instances through local transformations derived from the deformation field. In experiments, we demonstrate the accuracy and runtime efficiency of our registration method. We also report on the use of our skill transfer approach in a public demonstration.