108 citations · 150 across the 18 of their papers we have counts for
5 papers · 1 filter
LEGS: Learning Efficient Grasp Sets for Exploratory Grasping
Letian Fu, Michael Danielczuk, Ashwin Balakrishna +4
While deep learning has enabled significant progress in designing general purpose robot grasping systems, there remain objects which still pose challenges for these systems. Recent…
IPC-GraspSim: Reducing the Sim2Real Gap for Parallel-Jaw Grasping with the Incremental Potential Contact Model
Chung Min Kim, Michael Danielczuk, Isabella Huang +1
Accurately simulating whether an object will be lifted securely or dropped during grasping is a longstanding Sim2Real challenge. Soft compliant jaw tips are almost universally used…
FogROS: An Adaptive Framework for Automating Fog Robotics Deployment
Kaiyuan, Chen, Yafei Liang +6
As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource…
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities
Shivin Devgon, Jeffrey Ichnowski, Michael Danielczuk +6
In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly. Kitting is a critical step as it can decrease downstream processing and…
A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration
Tae Myung Huh, Kate Sanders, Michael Danielczuk +4
We present a novel robot end-effector for gripping and haptic exploration. Tactile sensing through suction flow monitoring is applied to a new suction cup design that contains mult…