Matthew Matl — accepted AI-conference papers

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 141 Source ↗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 233 Source ↗code ↗Scholar ↗

2018

Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

Jeffrey Mahler, Matthew Matl, Xinyu Liu, Albert Li, David Gealy, Ken Goldberg

ICRA 2018poster

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 of contact. Suction grasp planners often target planar surfaces on point clouds near the estimated centroid of an object.…

Cited by 690 Source ↗code ↗Scholar ↗

2017

A cloud robot system using the dexterity network and berkeley robotics and automation as a service (Brass)

Nan Tian, Matthew Matl, Jeffrey Mahler, Yu Xiang Zhou, Samantha Staszak, Christopher Correa +4

ICRA 2017poster

In support of Cloud Robotics, Robotics and Automation as a Service (RAaaS) frameworks have the potential to reduce the complexity of software development, simplify software installation and maintenance, and facilitate data sharing for machine learning. In this proof-of-concept paper, we describe Ber…

Cited by 47 Source ↗Scholar ↗