AIConfPaper
← Search

Michael Danielczuk

14 accepted papers

2023

FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2

Jeffrey Ichnowski, Kaiyuan Chen, Karthik Dharmarajan, Simeon Adebola, Michael Danielczuk, Víctor Mayoral-Vilches +9
ICRA 2023poster

Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run contemporary robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power and increasingly low latency on demand, but tapping i…

Cited by 25Source ↗code ↗Scholar ↗
2022

IPC-GraspSim: Reducing the Sim2Real Gap for Parallel-Jaw Grasping with the Incremental Potential Contact Model

Chung Min Kim, Michael Danielczuk, Isabella Huang, Ken Goldberg
ICRA 2022poster

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 with parallel-jaw robot grippers due to their ability to increase contact area and friction between the jaws and the ob…

Cited by 21Source ↗Scholar ↗
2022

LEGS: Learning Efficient Grasp Sets for Exploratory Grasping

Letian Fu, Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Jeffrey Ichnowski, Eugen Solowjow +1
ICRA 2022poster

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 work on Exploratory Grasping has formalized the problem of systematically exploring grasps on these adversarial objects…

Cited by 14Source ↗Scholar ↗
2022

Mechanical Search on Shelves using a Novel “Bluction” Tool

Huang Huang, Michael Danielczuk, Chung Min Kim, Letian Fu, Zachary Tam, Jeffrey Ichnowski +3
ICRA 2022poster

Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency. However, this efficiency comes at the cost of reduced visibility and accessibility. When looking from a side (lateral) view of a shelf, most objects will be fully occluded, resulting in a constrained la…

Cited by 24Source ↗Scholar ↗
2021

A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration

Tae Myung Huh, Kate Sanders, Michael Danielczuk, Monica Li, Yunliang Chen, Ken Goldberg +1
IROS 2021poster

We present a novel robot end-effector for gripping and haptic exploration. Tactile sensing through suction flow monitoring is achieved with a new suction cup design that contains multiple chambers for air flow. Each chamber connects with its own remote pressure transducer, which enables both absolut…

Cited by 28Source ↗Scholar ↗
2021

Mechanical Search on Shelves using Lateral Access X-RAY

Huang Huang, Marcus Dominguez-Kuhne, Vishal Satish, Michael Danielczuk, Kate Sanders, Jeffrey Ichnowski +4
IROS 2021poster

Finding an occluded object in a lateral access environment such as a shelf or cabinet is a problem that arises in many contexts such as warehouses, retail, healthcare, shipping, and homes. While this problem, known as mechanical search, is well-studied in overhead access environments, lateral access…

Cited by 32Source ↗Scholar ↗
2021

Object Rearrangement Using Learned Implicit Collision Functions

Michael Danielczuk, Arsalan Mousavian, Clemens Eppner, Dieter Fox
ICRA 2021poster

Robotic object rearrangement combines the skills of picking and placing objects. When object models are unavailable, typical collision-checking models may be unable to predict collisions in partial point clouds with occlusions, making generation of collision-free grasping or placement trajectories c…

Cited by 97Source ↗Scholar ↗
2020

6DFC: Efficiently Planning Soft Non-Planar Area Contact Grasps using 6D Friction Cones

Jingyi Xu, Michael Danielczuk, Eckehard Steinbach, Ken Goldberg
ICRA 2020poster

Analytic grasp planning algorithms typically approximate compliant contacts with soft point contact models to compute grasp quality, but these models are overly conservative and do not capture the full range of grasps available. While area contact models can reduce the number of false negatives pred…

Cited by 10Source ↗Scholar ↗
2020

Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects

Michael Danielczuk, Ashwin Balakrishna, Daniel Brown, Ken Goldberg
CoRL 2020

There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies. 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 qual

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2020

GOMP: Grasp-Optimized Motion Planning for Bin Picking

Jeffrey Ichnowski, Michael Danielczuk, Jingyi Xu, Vishal Satish, Ken Goldberg
ICRA 2020poster

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 on optimizing over a set of candidate grasps. The source of this set of grasps is two-fold: (1) grasp-analysis tools suc…

Cited by 63Source ↗Scholar ↗
2020

Minimal Work: A Grasp Quality Metric for Deformable Hollow Objects

Jingyi Xu, Michael Danielczuk, Jeffrey Ichnowski, Jeffrey Mahler, Eckehard Steinbach, Ken Goldberg
ICRA 2020poster

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 to damage it or dislodge liquids. We propose minimal work as a novel grasp quality metric that combines wrench resistanc…

Cited by 28Source ↗Scholar ↗
2020

X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions

Michael Danielczuk, Anelia Angelova, Vincent Vanhoucke, Ken Goldberg
IROS 2020poster

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. For mechanical search, we introduce X-Ray, an algorithm based on learned occupancy distributions. We train a neural network using…

Cited by 46Source ↗Scholar ↗
2019

Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter

Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna, Matthew Matl, David Wang, Roberto Martín-Martín +3
ICRA 2019poster

When operating in unstructured environments such as warehouses, homes, and retail centers, robots are frequently required to interactively search for and retrieve specific objects from cluttered bins, shelves, or tables. Mechanical Search describes the class of tasks where the goal is to locate and…

Cited by 141Source ↗Scholar ↗
2019

Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data

Michael Danielczuk, Matthew Matl, Saurabh Gupta, Andrew Li, Andrew Lee, Jeffrey Mahler +1
ICRA 2019poster

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 Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive hand-labeled datasets are…

Cited by 233Source ↗code ↗Scholar ↗