277 citations · 305 across the 7 of their papers we have counts for
7 papers
Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
Kate Sanders, Michael Danielczuk, Jeffrey Mahler +2
A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce. To accommodate a wide variety of products, many autom…
Industrial Robot Grasping with Deep Learning using a Programmable Logic Controller (PLC)
Eugen Solowjow, Ines Ugalde, Yash Shahapurkar +5
Universal grasping of a diverse range of previously unseen objects from heaps is a grand challenge in e-commerce order fulfillment, manufacturing, and home service robotics. Recent…
Minimal Work: A Grasp Quality Metric for Deformable Hollow Objects
Jingyi Xu, Michael Danielczuk, Jeff Ichnowski +3
Robot grasping of deformable hollow objects such as plastic bottles and cups is challenging as the grasp should resist disturbances while minimally deforming the object so as not t…
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
Michael Danielczuk, Matthew Matl, Saurabh Gupta +4
The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that…
Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning
Jeffrey Mahler, Matthew Matl, Xinyu Liu +3
Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point o…
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz +5
To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp…
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations
Michael Laskey, Caleb Chuck, Jonathan Lee +5
Motivated by recent advances in Deep Learning for robot control, this paper considers two learning algorithms in terms of how they acquire demonstrations. "Human-Centric" (HC) samp…