Adaptive Bayesian Optimization for Robotic Pushing of Thin Fragile Deformable Objects

Robotic manipulation of deformable objects is challenging due to the great variety of materials and shapes. This task is even more complex when the object is also fragile, and the allowed amount of deformation needs to be constrained. For the goal of driving a thin fragile deformable object to a target 2D position and orientation, we propose a manipulation method based on executing planar pushing actions on the object edges with a robotic arm. Firstly, we obtain a probabilistic model through Gaussian process regression, which represents the time-varying deformation properties of the system. Then, we exploit the model in the framework of an Adaptive Bayesian Optimization (ABO) algorithm to compute the pushing action at each instant. We evaluate our proposal in simulation.