Angelos Mavrogiannis

I am a PhD student in the department of Computer Science at the University of Maryland, College Park. I am a member of the Perception and Robotics Group where I work in the intersection of Robotics and Natural Language Processing, advised by Prof. Yiannis Aloimonos.

Prior to this, I received a Master of Science in Mechanical Engineering from Carnegie Mellon University. At CMU, I was a member of the Intelligent Control Lab in the Robotics Institute and my thesis focused on driver behavior classification, advised by Prof. Changliu Liu. Prior to CMU, I received my diploma (BS & MEng) in Mechanical Engineering and Aeronautics from the University of Patras (Greece). For my diploma thesis, I worked with Prof. Argyris Dentsoras on the development of a framework for automatic parsing and solution of optimization problems formulated in structured language.

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I am interested in the intersection of Robotics and Natural Language Processing (RoboNLP), and more specifically in leveraging linguistic traits to enrich robots with common-sense reasoning.


April 2024: Patent application filed for work I was involved in at LinkedIn during my summer 2023 internship.
March 2024: Cook2LTL was accepted for publication at IEEE ICRA 2024 and will be presented in Yokohama, Japan in May 2024.
November 2023: I am returning to LinkedIn for a summer 2024 internship in San Francisco.

robot_video Language, Perception, and Action for Attribute Detection
Angelos Mavrogiannis, Dehao Yuan, Yiannis Aloimonos
Under Review
arXiv coming soon / code coming soon / video coming soon

Projecting language and perception to a joint function-centric programmatic representational domain enables the emergence of complementary reasoning capabilities for attribute detection through information passing between foundation model API calls. We demonstrate the applicability of this representation in embodied scenarios by integrating it with a robot action API and deploying an open-source end-to-end framework that uses this representation on a real robot. The robot leverages its onboard sensors and combines language, perception, and action into a unified representation to execute attribute detection-driven natural language instructions.

cook2ltl_cover Cook2LTL: Translating Cooking Recipes to LTL Formulae using Large Language Models
Angelos Mavrogiannis, Christoforos Mavrogiannis, Yiannis Aloimonos
IEEE International Conference on Robotics and Automation (ICRA), 2024   (Oral Presentation, ICRA 2024, Yokohama, Japan)
arXiv / code / video / poster

Cook2LTL is a system that receives a cooking recipe in natural language form, reduces high-level cooking actions to robot-executable primitive actions through the use of LLMs, and produces unambiguous task specifications written in the form of LTL formulae.

b-gap simulation gif B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving
Angelos Mavrogiannis, Rohan Chandra, Dinesh Manocha
IEEE Robotics and Automation Letters (RA-L), 2022   (Oral Presentation, IROS 2022, Kyoto, Japan)
(IROS Student and Developing Countries Travel Award)
paper / arXiv / website / code / video

B-GAP is a new simulation technique consisting of enriching existing traffic simulators with behavior-rich trajectories corresponding to varying levels of aggressiveness. After generating these trajectories with the help of a driver behavior modeling algorithm, we use an enriched simulator to train a Deep Reinforcement Learning (DRL) policy that consists of a set of high-level vehicle control commands and apply this policy at test time to perform local navigation in dense traffic.

driver_behavior_kmeans Human Driver Behavior Classification from Partial Trajectory Observation
Angelos Mavrogiannis, Changliu Liu
Technical Report, 2020   (Carnegie Mellon Mechanical Engineering Research Symposium Award)
ResearchGate / code / video

We extract high-level features from raw vehicle trajectory data and classify drivers into behavioral classes based on their level of aggressiveness. We demonstrate how the identification of a driver's behavior improves the accuracy of the short-term trajectory prediction problem by introducing a prior knowledge on their behavior.

finger_forces Genetic-Algorithm-based Optimization Framework
Angelos Mavrogiannis, Argyris Dentsoras
Technical Report, 2017
report (in greek) / code

We developed a framework on Visual Basic that receives mathematical expressions as input, analyzes them using a suitable parser, and optimizes them with genetic algorithms. The parser allows the input of the expressions in string format and distinguishes the variables, the parameters and the operational symbols. Besides the equations, the user can choose between a set of genetic algorithms for the optimization, as well as the hyperparameters. The implemented software was tested and validated on two applications: the minimization of the forces applied onto an object grasped by a robotic arm and the maximization of the stiffness of a cantilever beam.

LinkedIn Internship Projects

In the summers of 2023 and 2022 I worked as an Artificial Intelligence - Machine Learning Engineer in the Ads AI organization at LinkedIn in Mountain View, California.

audience_targeting Audience Targeting using Open-Source Large Language Models
Angelos Mavrogiannis, Jiarui Wang, Jinghui Mo, Alice Wu
Internship Project, 2023 (Patent application filed in April 2024)

My summer 2023 intern project introduced open-source generative AI in the current LinkedIn audience targeting pipeline. I used Low Rank Adaptation (LoRA) to fine-tune state-of-the-art open-source Large Language Models, quantitatively compared them and identified the best performing task-specific model. Fine-tuning this model, I outperformed the existing baseline used in production for automatic audience targeting in user-defined advertising campaigns.

linkedin_auction Automatic Bidding using Deep Reinforcement Learning
Angelos Mavrogiannis, Yuanlong Chen, Min Liu
Internship Project, 2022

I developed a DRL framework for multi-constraint automatic bidding towards optimizing the LinkedIn Ads platform. The framework was built upon OpenAI gym, using synthetic data to simulate a 2nd price auction real-time bidding environment. This project was the stepping stone for a DRL-based system that was subsequently used in production for automatic bidding at LinkedIn.

Course Projects

Representative group course projects at UMD and CMU. For more projects check my CV.

interview Evaluating the Fairness of Diffusion-based Face Generation from Conversational Text - A Pilot Study
Angelos Mavrogiannis, Vishnu Shashank Dorbala
Technical Report, UMD CMSC 828I: Trustworthy Machine Learning, Fall 2023

In this work we measure the fairness of diffusion-based face generation models powered by conversational input data in textual form. We design a pilot study where participants answer LLM-generated non-intrusive questions about themselves with the goal of implicitly extracting facial features to build an informed prompt for face generation.

vr_racetrack_setup VR-Integrated Real-Time Racetrack Simulator
Angelos Mavrogiannis, Zining Zhang, Logan Stevens, Elliot Huang, Hyekang Kevin Joo
Technical Report, UMD CMSC 730: Interactive Technologies in HCI, Fall 2022

Proposed and led a group project on building a 3D-printed chessboard-resembling racetrack and an interactive system that converts it to a VRsimulated racing environment. Implemented the computer vision module, tracking the position and orientation of the pieces using ArUco markers and mapping them to poses and ego-vehicle control commands in a simulated racetrack in Unity.

suicidality_prediction_pipeline Predictive Modeling Using Linguistic Signal for Suicidality in Social Media
Angelos Mavrogiannis, Divya Kothandaraman, Sanket Doshi, Yashish Maduwantha, Anchit Jain
Technical Report, UMD CMSC 723: Computational Linguistics, Spring 2021

This work uses a Hierarchical Attention Network architecture to assess the potential suicide risk of reddit users based on their post history. My contribution was the extraction of post-level features based on users’ emotional states and the tuning of a Latent Dirichlet Allocation (LDA) model to retrieve meaningful subreddit clusters.

underwater_robot Bioinspired Robot Design
Angelos Mavrogiannis, Pranav Narahari, Aniruddhan Unni, William Moreno, Stam Athiniotis
Technical Report, CMU 24-775: Robot Design & Experimentation, Spring 2019
report / video

Collaborated with a team of students to design and manufacture an underwater penguin-inspired robot. The movement of the flippers was powered by an Arduino microcontroller controlling a ball-and-socket motion transmission mechanism.

openai_gym_gripper_object Object Pose Estimation from Manipulator Pose
Angelos Mavrogiannis, Satyaki Chakraborty, Suman Pokharel
Technical Report, CMU 16-741: Mechanics of Manipulation, Fall 2018

Collected a synthetic dataset of manipulator postures and object poses in OpenAI Gym. Trained a multilayer perceptron in order to map changes in hand pose to object displacements. Modified the OpenAI Gym simulator to demonstrate the predicted object pose and validated the method on occluded object tracking problems.

Summer Schools

disc_summer_school The Machine Learning Summer School in Okinawa 2024
Machine Learning Summer Schools (MLSS), Okinawa Institute of Science and Technology (OIST), Riken AIP
March 4-15, 2024, Okinawa, Japan (NeuroPAC AccelNet travel fellowship)
hri_summer_school_poster 4th Summer School on Social Human-Robot Interaction
Human Interactivity and Language Lab, Faculty of Psychology, University of Warsaw, IEEE Robotics & Automation Society
September 18-23, 2023, Chęciny, Poland (IEEE Robotics & Automation Society scholarship)
disc_summer_school DISC Summer School 2021 for Planning, Learning and Control for Multi-Robot and Multi-Agent Systems
Dutch Institute of Systems and Control
June 8-11, 2021, remote
robotics_ai_summer_school_2021 Robotics & AI Summer School
IRI - Institut de Robòtica i Informàtica Industrial
June 28-30, 2021, remote
3rd_summerschool_acm 3rd ACM Summer School in Data Science
Association for Computing Machinery, Athena Research and Innovation Center
July 11-17, 2019, Athens, Greece

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