most citedDeep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor

9 citations · 11 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO20201 cited

GOMP: Grasp-Optimized Motion Planning for Bin Picking

Jeffrey Ichnowski, Michael Danielczuk, Jingyi Xu +2

Rapid and reliable robot bin picking is a critical challenge in automating warehouses, often measured in picks-per-hour (PPH). We explore increasing PPH using faster motions based…

cs.RO2020

Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks

Minho Hwang, Brijen Thananjeyan, Samuel Paradis +5

Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in…

cs.RO2020

Applying Depth-Sensing to Automated Surgical Manipulation with a da Vinci Robot

Minho Hwang, Daniel Seita, Brijen Thananjeyan +5

Recent advances in depth-sensing have significantly increased accuracy, resolution, and frame rate, as shown in the 1920x1200 resolution and 13 frames per second Zivid RGBD camera.…

cs.RO20199 cited

Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor

Daniel Seita, Aditya Ganapathi, Ryan Hoque +11

Sequential pulling policies to flatten and smooth fabrics have applications from surgery to manufacturing to home tasks such as bed making and folding clothes. Due to the complexit…

cs.RO20191 cited

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…