Adam Conkey

PhD Student in Robotics

University of Utah

Biography

I am a PhD student in the School of Computing at the University of Utah in the robotics track of the Utah Robotics Center. I work in the Utah Learning Lab for Manipulation Autonomy (LL4MA) and am advised by Dr. Tucker Hermans. My current research focuses on applying deep learning to problems in multisensory perception to better understand how sensory observations correspond to task-level events. I am also interested in investigating new methods for integrating perception and action for robotic agents.

I have hands-on experience with a variety of robot platforms, sensors, and simulators. I am passionate about open source software development for robotics, particularly for the ROS framework. Please have a look at my resume and feel free to contact me if you would like to discuss future job opportunities.

Interests

Robotics
Artificial Intelligence
Multisensory Perception
Deep Learning
Active Inference
Embodied Cognition

Education

PhD in Computing and Robotics, 2021

University of Utah
MS in Computer Science, 2016

DePaul University
BS in Mathematics and Philosophy, 2011

Carnegie Mellon University

Experience

Research Scientist Intern - Advanced Robotics

Amazon Robotics

May 2019 – Aug 2019 North Reading, MA

Associate Software Engineer

Accenture

Apr 2015 – Jul 2016 Austin, TX

Patent Examiner - Computer Science

United States Patent and Trademark Office

May 2012 – Nov 2013 Alexandria, VA

Publications

Adam Conkey, Tucker Hermans

October 2019 Humanoids 2019

Active Learning of Probabilistic Movement Primitives

An active learning approach to learning a library of Probabilistic Movement Primitives capable of generalizing over a bounded space.

PDF Code Dataset Video

Adam Conkey, Tucker Hermans

October 2019 Humanoids 2019

Learning Task Constraints from Demonstration for Hybrid Force/Position Control

We learn a dynamic constraint frame from demonstration for hybrid force/position control using Cartesian Dynamic Movement Primitives.

PDF Code Dataset Video

Software Projects

Software projects I have implemented over the course of my PhD to support my research.

Robot Control in Gazebo

Robot control framework for simulations in Gazebo. Designed to be extensible for easily adding new controllers. Works in conjunction with my robot interface package to decouple the controllers from any particular robot.

Code

Robot Interface

Abstraction layer for controlling robots in a unified manner while accounting for robot platform-specific needs.

Code

Real-Time Robot Control

Real-time Orocos controllers for the KUKA LBR4+ robot. Includes a controller switching framework for safely swapping controllers at runtime, and a simulated FRI component that allows Orocos components to be tested Gazebo.

Code

ReFlex TakkTile in Gazebo Simulation

An actuated version of the ReFlex TakkTile hand and Gazebo plugins for simulating contact and pressure sensors on the fingers.

Code

Simulator for Teleoperation

A lightweight simulator with force rendering created with rviz and DART. Provides a training ground for learning robust policies from demonstration with a haptic input device.

Code

Baxter Demo Recorder

A demonstration recorder for the Baxter robot utilizing the button and display interfaces to make it easier to record demonstrations when operating Baxter in gravity compensation mode.

Code

rviz Trajectory Visualization

Package for generating trajectory visualizations in rViz, including dynamic real-time visualizations showing a customizable trace of the robot’s end-effector.

Code