Andrea F. Daniele
Chief Technology Officer • Robotics Engineer • Computer Scientist
Work Experience
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Chief Technology Officer
Nov. 2022 - present
I took the position of CTO at Duckietown, where I lead the design and development of all new Duckietown robots and software products.
Boston, MA, USA
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Lead Software Engineer
Jan. 2021 - Nov. 2022
Officially joined the Duckietown Team as Lead UX Software Engineer. I led the effort of improving and developing new software modules aimed at improving the user experience (UX) on the Duckietown robots.
Zürich, Switzerland
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Software Engineer Intern
Jan. 2019 - May 2019
During a 4-months internship as a Software Engineer in the Simulation Team at drive.ai, I worked on the problem of generating behaviors for dynamic simulated agents using semantically rich graphs computed from annotated HD maps.
Mountain View, CA, USA
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Software Engineer
Sep. 2017 - Jan. 2021
I contributed to the Duckietown project as a volunteer at the Duckietown Foundation. I led the effort of improving accessibility in robot programming, debugging, and monitoring with the development of better user interfaces.
Chicago, IL, USA
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Software Engineer
Aug. 2013 - Jan. 2016
Developed and implemented Web-based applications, websites, web-APIs, and interactive applications for desktop environments and mobile devices.
Cloud4Service.net
Petilia Policastro, KR, Italy
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Education
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Ph.D. in Computer Science
Sep. 2016 - Sep. 2023
Toyota Technological Institute at Chicago
Chicago - IL - USA
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M.S. in Computer Science
Sep. 2016 - Sep. 2019
Toyota Technological Institute at Chicago
Chicago - IL - USA
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M.S. in Artificial Intelligence and Robotics
Oct. 2013 - Dec. 2016
University of Rome - La Sapienza
Rome - RM - Italy
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B.S. in Computer Engineering
Oct. 2009 - Jul. 2013
University of Calabria - UNICAL
Rende - CS - Italy
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Portfolio
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Duckiematrix
A high-performance robot simulator
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Roles:
Lead Architect
Main Developer
The Duckiematrix is a high-performance robot simulator
for the Duckietown platform that allows for the simulation of large fleets of robots
in real-time. It is based on the Unity
game engine.
The Duckiematrix supports the concurrent simulation of multiple robots,
such as ground vehicles,
drones, smart city infrastructure,
and all their sensors and actuators.
It is also designed to support the use of multiple renderers
to distribute the rendering load across multiple GPUs or nodes across the network.
Learn more from the official documentation.
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DD24 Autonomous Drone
A state-of-the-art educational autonomous drone
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Roles:
Project Leader
The
DD24
autonomous drone is a state-of-the-art educational platform by
Duckietown, Inc.
With the DD24, we wanted to create a drone that was
easy to use, program, debug, and understand.
At the same time, we wanted a drone that was powerful enough
to be used in advanced robotics courses and research projects.
Compared to its predecessor, the DD21, the DD24 is 36% more powerful,
30% smaller, has 3x the number of sensors, and new obstacle detection
capabilities with 270deg coverage.
Learn more from the official documentation.
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Interactive Robot Assembly Tool
An interactive tool for assembling robots in 3D
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Roles:
Lead Architect
Main Developer
The Interactive Robot Assembly Tool is a web-based tool that allows users to
assemble physical robots while following the step-by-step assembly process in 3D.
The view can be rotated, zoomed, and panned to provide a better understanding of
all the steps that make up the assembly process.
The Interactive Robot Assembly Tool is based on the
Unity game engine. This provides
a photo-realistic rendering engine in which high-fidelity 3D models of the robot
components can be used.
Learn more from the official video
and
live example.
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SHARC
Shared autonomy for robotic underwater exploration
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Roles:
Co-Inventor
Current Owner / License:
Patented
Conventional underwater intervention operations
using robotic vehicles require expert teleoperators on a support vessel
deployed nearby the intervention site.
SHARC (SHared Autonomy for Remote Collaboration) is a framework that
enables operators to cooperatively conduct robotic underwater sampling
and manipulation tasks from thousands of miles away.
With SHARC, operators can execute manipulation tasks using natural language or hand
gestures through a virtual reality interface.
SHARC is readily extensible to other tasks and domains such as planets exploration.
Learn more from our
scientific publication
or the
project page.
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The Duckietown Dashboard
A browser-accessible control center and robot data visualizer
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Roles:
Lead Architect
Main Developer
The Duckietown Dashboard is a browser-accessible control center and robot data visualizer
for the Duckietown platform.
I started the development of the Dashboard in 2017 as a tool to help
students and researchers to interact with the Duckietown robots in a more intuitive way.
It is now a key component of the Duckietown platform and is used by thousands of users
worldwide.
The Duckietown Dashboard is created using the \compose\ framework.
Learn more from the official documentation.
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aavm
Almost A Virtual Machine
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Roles:
Lead Architect
Main Developer
Current Owner / License:
MIT License
While Virtual Machines are a powerful tool for isolated deployments,
they are often too heavy to justify their use for simple applications.
The Almost A Virtual Machine (aavm) is a lightweight alternative to full-blown
VMs that allows you to run applications in sandboxed environments
with minimal overhead.
aavm uses Docker containers configured with an isolated instance of systemd
to provide a full Linux environment for your applications.
An example use case is that of running applications that usually require
a full desktop environment on headless systems, e.g., Unity applications.
aavm can, in fact, run instances of the X server with GPU access on systems without a display.
Learn more from the official GitHub repository.
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\compose\
A lightweight web-based CMS for robot telemetry
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Roles:
Lead Architect
Main Developer
Current Owner / License:
MIT License
\compose\ is an open-source web-based
CMS platform that provides all the functionalities needed for the fast development
and deployment of web applications. In particular, it focuses on the development
of web applications that require real-time data visualization and interaction.
This makes it the ideal framework for the development of robot telemetry dashboards
and control centers.
An example use case is the Duckietown Dashboard.
\compose\ is written in PHP and designed to be lightweight, modular, and easy to use.
Learn more from the official GitHub repository.
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cpk
the Code Packaging toolKit
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Roles:
Lead Architect
Main Developer
Current Owner / License:
MIT License
cpk stands for Code Packaging toolKit and is designed to standardize
the way code in a project is structured and packaged for maximum portability,
readability and maintainability.
cpk is the result of years of experience in cross-user, cross-machine,
cross-architecture development and deployment of software modules.
cpk organizes code in projects. A cpk project is a directory containing everything that
is needed for the project to be built, packaged, documented and deployed.
An easy-to-use command-line interface is provided to manage the project. It leverages
the power of Docker to provide
consistent and reproducible, yet dynamic development and deployment environments.
Learn more from the official GitHub repository.
Events
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Arctic Code Vault
The cold storage that will last 1,000 years
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Some of my open-source contributions were selected for archival in the
Arctic World Archive (AWA),
an archival facility designed to preserve data for 1,000 years.
A total of 186 reels of film were stored in a steel-walled container, inside a sealed chamber,
within a decommissioned coal mine, buried deep into the permafrost of the Svalbard archipelago,
in Norway. Internationally recognized as a demilitarized zone, Svalbard is the world’s
northernmost town and one of the most remote and geopolitically stable human habitations on Earth.
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SHARC: SHared Autonomy for Remote Collaboration at WHOI
August 2021 - October 2021
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I worked on the development of "SHARC: SHared Autonomy for Remote Collaboration"
at the Woods Hole Oceanographic Institution.
SHARC is a multi‑modal interface that enables remote scientists to perform high‑level tasks
using an underwater manipulator, while deferring low‑level control to the robot.
We successfully deployed and tested SHARC during the
OECI Technology Demonstration: Nereid Under Ice (NUI) Vehicle + Mesobot
expedition aboard E/V Nautilus.
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March 2021 - August 2021
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I co-organized the first hardware based massive online open course (MOOC)
in AI and robotics, free on edX.
Aimed at teaching autonomy hands-on by making robots that can take their
own decisions and accomplish broadly defined tasks.
The course guides learners step-by-step from the theory, to the
implementation, to the deployment in simulation as well as on real
robots (Duckiebots).
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Internship at drive.ai - Software Engineer - Simulation Team
January 2019 - May 2019
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Leveraging static annotations and HD maps, we created a semantically rich lane-level graph,
that simulated agents can use to navigate the map. This lane graph is comprised of three layers:
topological, metrical, and semantic.
Topological and metrical layers were extracted from HD maps and static annotations,
while the semantic layer was constructed from annotations only.
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Workshop on Models and Representations for Natural Human-Robot Communication at the RSS18 Conference
June 2018
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UR5-equipped Robot playing Checkers, 2018 National Robotics Week at MSI
April 2018
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Under the supervision of my adviser
Prof. Matthew Walter,
I and other colleagues
showed our UR5-equipped Husky A200 robot safely playing Checkers against human
opponents at the Museum of Science and Industry
in Chicago for the 2018 National Robotics Week exhibit. This robot is developed in the
RIPL lab at
TTI-Chicago.
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Duckietown 2017
September 2017
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Husky A200 robot for the National Robotics Week at the MSI-Chicago
April 2017
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Under the supervision of my adviser
Prof. Matthew Walter,
I and other colleagues
showed our Husky A200 robot at the Museum of Science and Industry in Chicago
for the National Robotics Week. This robot is developed in the
RIPL lab at
TTI-Chicago as part of the
Robotics Collaborative Technology Alliance (RCTA)
research program. CBS 2’s Vince Gerasole interviewed me on that occasion.
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NLIGEN - Natural Language Instruction Generation
January 2016 - September 2016
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Under the supervision of my adviser
Prof. Matthew Walter,
I developed a model that enables
robots to generate natural language instructions that
allow humans to navigate a priori unknown environments.
The model first decides which information to share with the user
according to their preferences, then “translates” this information
into a natural language instruction.
Publications
A Shared Autonomy System for Precise and Efficient Remote Underwater Manipulation.
Amy Phung, Gideon Billings, Andrea F. Daniele, Matthew R Walter, and Richard Camilli.
IEEE, 2024.
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Enhancing scientific exploration of the deep sea through shared autonomy in remote manipulation.
Amy Phung, Gideon Billings, Andrea F. Daniele, Matthew R Walter, and Richard Camilli.
Science Robotics - Vol 8 / No 81, 2023.
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Ph.D. Thesis - Accessible Interfaces for the Development and Deployment of Robotic Platforms.
Andrea F. Daniele.
Ph.D. Thesis, 2023.
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Toward Efficient Under-Ice Exploration of Ocean Worlds Using Distributed Autonomy and 3D Workspace Reconstruction Presented in VR for Intuitive Understanding.
Amy Phung, Gideon Billings, Andrea F. Daniele, Matthew Walter, and Richard Camilli.
The Astrobiology Science Conference (AbSciCon) 2022, 2022.
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Language Understanding for Field and Service Robots in a Priori Unknown Environments.
Matthew R Walter, Siddharth Patki, Andrea F. Daniele, Ethan Fahnestock, Felix Duvallet, Sachithra Hemachandra, Jean Oh, Anthony Stentz, Nicholas Roy, and Thomas M. Howard.
Journal of Field Robotics (IJFR 2021), 2021.
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Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents.
Jacopo Tani, Andrea F. Daniele, Gianmarco Bernasconi, Amaury Camus, Aleksandar Petrov, Anthony Courchesne, Bhairav Mehta, Rohit Suri, Tomasz Zaluska, Matthew R. Walter, Emilio Frazzoli, Liam Paull, and Andrea Censi.
In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS 2020), July 2020.
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DIODE: A Dense Indoor and Outdoor DEpth Dataset.
Igor Vasiljevic, Nick Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, and Gregory Shakhnarovich.
CoRR volume abs/1908.00463, August 2019.
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Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions.
Siddharth Patki, Andrea F. Daniele, Matthew R. Walter, and Thomas M. Howard.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2019, May 2019.
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The AI Driving Olympics at NeurIPS 2018.
Julian Zilly, Jacopo Tani, Breandan Considine, Bhairav Mehta, Andrea F. Daniele, Manfred Diaz, Gianmarco Bernasconi, Claudio Ruch, Jan Hakenberg, Florian Golemo, A. Kirsten Bowser, Matthew R. Walter, Ruslan Hristov, Sunil Mallya, Emilio Frazzoli, Andrea Censi, and Liam Paull.
arXiv:1903.02503, March 2019.
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A Multiview Approach to Learning Articulated Motion Models.
Andrea F. Daniele, Thomas M. Howard, and Matthew R. Walter.
In Proceedings of the International Symposium of Robotics Research (ISRR), 2017, December 2017.
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Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation.
Andrea F. Daniele, Mohit Bansal, and Matthew R. Walter.
In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI), March 2017.
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Natural Language Generation in the Context of Providing Indoor Route Instructions.
Andrea F. Daniele, Mohit Bansal, and Matthew R. Walter.
In Proceedings Robotics: Science and Systems Workshop on Model Learning for Human-Robot Communication, May 2016.
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