Search Results for author:
Found 10 papers, 3 papers with code
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
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
Object Rearrangement Using Learned Implicit Collision Functions
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.
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities
In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly.
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
In this paper, we formalize Mechanical Search and study a version where distractor objects are heaped over the target object in a bin.
Robotics
GOMP: Grasp-Optimized Motion Planning for Bin Picking
Rapid and reliable robot bin picking is a critical challenge in automating warehouses, often measured in picks-per-hour (PPH).
Robotics
X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions
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.
Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce.
Accelerating Grasp Exploration by Leveraging Learned Priors
The ability of robots to grasp novel objects has industry applications in e-commerce order fulfillment and home service.
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects
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.
Mechanical Search on Shelves using a Novel "Bluction" Tool
Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency.
