277 citations · 305 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…