Guided pushing for object singulation

We propose a novel method for a robot to separate and segment objects in a cluttered tabletop environment. The method leverages the fact that external object boundaries produce visible edges within an object cluster. We achieve this singulation of objects by using the robot arm to perform pushing actions specifically selected to test whether particular visible edges correspond to object boundaries. We verify the separation of objects after a push by examining the clusters formed by geometric segmentation of regions residing on the table surface. To avoid explicitly representing and tracking edges across push behaviors we aggregate over all edges in a given orientation by representing the push-history as an orientation histogram. By tracking the history of directions pushed for each object cluster we can build evidence that a cluster cannot be further separated. We present quantitative and qualitative experimental results performed in a real home environment by a mobile manipulator using input from an RGB-D camera mounted on the robot's head. We show that our pushing strategy can more reliably obtain singulation in fewer pushes than an approach, that does not explicitly reason about boundary information.

Authors

• Tucker Hermans
• James M. Rehg
• Aaron F. Bobick

Keywords

• Image edge detection
• Vectors
• History
• Histograms
• Motion segmentation
• Image segmentation
• Qualitative Results
• Robotic Arm
• Object Boundaries
• Separate Objects
• Mobile Manipulator
• Orientation Histogram
• Workspace
• Object Recognition
• Point Cloud
• Image Intensity
• Binary Image
• Single Object
• Successful Trials
• Depth Images
• End-effector
• Image Edge
• Potential Objects
• Start Location
• Iterative Closest Point
• Histogram Bins
• Potential Boundary
• Low Texture
• High Texture
• Input Point Cloud
• Highest Success Rate
• 3D Line
• Edge Extraction
• Optical Flow
• Texture Of Objects