Competitive Advantages
- Capable of transferring multiple objects
- Based on deep learning model for accurate predictions
- Reaches the desired quantity while minimizing the number of grasps and transfers between bins
Summary
Transferring multiple objects between bins is a common task for many applications. In robotics, a standard approach is to pick up one object and transfer it at a time. However, grasping and picking up multiple objects and transferring them together at once is more efficient. Researchers have developed new multi-object grasping (MOG) techniques for transferring a large quantity of uniformly shaped and sized objects from one bin to another. The MOG techniques include clustered-probability-based pre-grasp, best expectation pre-grasp, maximum capability pre-grasp, and a data-driven deep learning model to predict the number of objects in a grasp after the hand lifts, when the hand is in the pile. These techniques, coupled with a Markov decision process-based transfer policy, minimize the number of grasps and transfers needed between bins while transferring large quantities of objects.
An example of multi-object grasping and transferring of 10 tomatoes from the blue bin to the yellow bin
Desired Partnerships
- License
- Sponsored Research
- Co-Development