In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly.
The learned model outperforms both traditional pipelines and learned ablations by 9. 8% in accuracy on a dataset of simulated collision queries and is 75x faster than the best-performing baseline.
However, these policies can consistently fail to grasp challenging objects which are significantly out of the distribution of objects in the training data or which have very few high quality grasps.
The ability of robots to grasp novel objects has industry applications in e-commerce order fulfillment and home service.
A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce.
For applications in e-commerce, warehouses, healthcare, and home service, robots are often required to search through heaps of objects to grasp a specific target object.
Rapid and reliable robot bin picking is a critical challenge in automating warehouses, often measured in picks-per-hour (PPH).
In this paper, we formalize Mechanical Search and study a version where distractor objects are heaped over the target object in a bin.
We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry.
Ranked #1 on Unseen Object Instance Segmentation on WISDOM