Multimodal feedback for active robot-object interaction
This addresses robot manipulation in cluttered environments, though it appears incremental as it combines existing sensor modalities.
The researchers tackled robot-object manipulation by developing a multimodal system combining RGBD vision and tactile feedback, which achieved the best performance compared to single-modality baselines in RoboCup2018 experiments.
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.