Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing
This addresses a specific bottleneck in robotic grasping for moving objects, but is incremental as it builds on existing visual servoing methods.
The paper tackles the problem of robotic grasping failing when depth sensors lose data near objects by using image-based visual servoing to guide the robot using only RGB information, enabling more reliable grasping of moving objects.
This paper considers the final approach phase of visual-closed-loop grasping where the RGB-D camera is no longer able to provide valid depth information. Many current robotic grasping controllers are not closed-loop and therefore fail for moving objects. Closed-loop grasp controllers based on RGB-D imagery can track a moving object, but fail when the sensor's minimum object distance is violated just before grasping. To overcome this we propose the use of image-based visual servoing (IBVS) to guide the robot to the object-relative grasp pose using camera RGB information. IBVS robustly moves the camera to a goal pose defined implicitly in terms of an image-plane feature configuration. In this work, the goal image feature coordinates are predicted from RGB-D data to enable RGB-only tracking once depth data becomes unavailable -- this enables more reliable grasping of previously unseen moving objects. Experimental results are provided.