Research Scientist Intern - Advanced Robotics

Amazon Robotics

May 2019 – Aug 2019 North Reading, MA

Associate Software Engineer


Apr 2015 – Jul 2016 Austin, TX

Patent Examiner - Computer Science

United States Patent and Trademark Office

May 2012 – Nov 2013 Alexandria, VA


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

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

Software Projects

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

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.

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

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.

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

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.

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.

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