Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

We consider a decluttering problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface and must be efficiently transported to a packing box using both single and multi-object grasps. Prior work considered frictionless multi-object grasping. In this paper, we introduce friction to increase the number of potential grasps for a given group of objects, and thus increase picks per hour. We train a neural network using real examples to plan robust multi-object grasps. In physical experiments, we find a 13. 7% increase in success rate, a 1. 6x increase in picks per hour, and a 6. 3x decrease in grasp planning time compared to prior work on multi-object grasping. Compared to single-object grasping, we find a 3. 1x increase in picks per hour.

Authors

• Wisdom C. Agboh
• Satvik Sharma
• Kishore Srinivas
• Mallika Parulekar
• Gaurav Datta
• Tianshuang Qiu
• Jeffrey Ichnowski
• Eugen Solowjow
• Mehmet Remzi Dogar
• Ken Goldberg

Keywords

• Friction
• Neural networks
• Grasping
• Planning
• Intelligent robots
• Neural Network
• Multiple Objects
• C=O Groups
• Physical Experiments
• Planning Time
• Convex Polygon
• Increased Success Rate
• Monte Carlo Simulation
• Random Sampling
• Artificial Neural Network
• Friction Coefficient
• Feed-forward Network
• Number Of Objects
• Single Object
• Convex Hull
• Data Collection System
• Minimum Diameter
• Low Friction
• Physical Simulation
• State Of Uncertainty
• Physical Robot
• Contact Pairs
• Final Diameter
• Intersection Area
• Friction Model
• Overhead Camera
• Frictional Contact
• Contact Model
• Input Vector
• Connecting Lines