  In this work, we present a multimodal system for active robot-object
interaction using laser-based SLAM, RGBD images, and contact sensors. In the
object manipulation task, the robot adjusts its initial pose with respect to
obstacles and target objects through RGBD data so it can perform object
grasping in different configuration spaces while avoiding collisions, and
updates the information related to the last steps of the manipulation process
using the contact sensors in its hand. We perform a series of experiment to
evaluate the performance of the proposed system following the the RoboCup2018
international competition regulations. We compare our approach with a number of
baselines, namely a no-feedback method and visual-only and tactile-only
feedback methods, where our proposed visual-and-tactile feedback method
performs best.
