Ryan Diaz
Hello! I am an incoming first-year CS Ph.D. student at Rice University. My research interests include designing human-centric learning algorithms for robotic manipulation, and I will be doing research with Prof. Vaibhav Unhelkar at the Human-Centered AI and Robotics Group. I'm always trying to explore new ways to help robots do cool things!
Previously, I was an undergraduate at the University of Minnesota where I worked with Prof. Karthik Desingh at the Robotics: Perception and Manipulation Lab on multisensory contact-rich robotic manipulation. I also got the chance to do research on reinforcement learning for autonomous driving with Prof. Yevgeniy Vorobeychik at the WashU CSE REU.
Outside of academics and research, I enjoy cooking, reading, and worldbuilding (as well as procrastinating way too much on actually writing). Whether it be writing papers, writing code, or writing stories, the keyboard always calls...
News
- [05/15/2025] I graduated from UMN with a B.S. in Computer Science and Mathematics, summa cum laude with high distinction!
- [05/01/2025] AugInsert was accepted to the Beyond Pick and Place Workshop @ ICRA 2025!
- [04/07/2025] I will be starting a CS Ph.D. at Rice University in Fall 2025, advised by Prof. Vaibhav Unhelkar!
- [12/28/2024] I was named an honorable mention for the 2025 CRA Outstanding Undergraduate Researcher Award!
- [10/22/2024] Our project AugInsert is now live on arXiv! You can find an overview video of the project here.
- [04/26/2024] This summer I am planning to go to the CSE REU at Washington University in St. Louis to work on algorithms for autonomous vehicle movement!
- [01/30/2024] Evaluating Robustness of Visual Representations was accepted to ICRA 2024!
- [12/10/2023] Presented a video for the Fall 2023 Undergraduate Research Symposium at UMN! You can find an abstract of the project and the video here.
- [10/23/2023] Evaluating Robustness of Visual Representations was accepted to the 2nd Pretraining for Robot Learning Workshop @ CoRL 2023!
- [04/25/2023] Presented a poster at the Spring 2023 Undergraduate Research Symposium at UMN! You can find an abstract of the project here.
Publications
AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks
Ryan Diaz, Adam Imdieke, Vivek Veeriah, Karthik Desingh
Beyond Pick and Place Workshop at ICRA 2025
[Paper]/ [Website]/ [Video]/ [Code]
We build a multisensory imitation learning framework and evaluate it on an extensive set of task variations for a peg-in-hole task. We also explore data augmentation as a possible technique for increasing a policy's robustness to these variations.
Evaluating Robustness of Visual Representations for Object Assembly Task Requiring Spatio-Geometrical Reasoning
Chahyon Ku, Carl Winge, Ryan Diaz, Wentao Yuan, Karthik Desingh
IEEE International Conference on Robotics and Automation (ICRA) 2024 2nd Pretraining for Robot Learning Workshop at CoRL 2023
[Paper]/ [Website]/ [Video]/ [Code]
We introduce a novel dual-arm object assembly task that focuses on geometric and spatial reasoning. We compare multiple pretrained vision encoders in a behavior cloning framework across a large set of grasp and object geometry variations.
Projects
Multisensory Visuotactile Pretraining for Robotic Manipulation Tasks
CSCI 5980: Deep Learning for Robot Manipulation (University of Minnesota)
We implement a masked pretraining objective for a vision and force-torque observation encoder and perform downstream evaluation on a series of contact-rich robotic manipulation tasks.
Obstacle Detection and Avoidance in Simulated Autonomous Driving
CSE REU Project (Washington University in St. Louis)
[Report]/ [Poster]/ [Detection Code]/ [RL Code]
We use an object detection module and reinforcement learning to build an obstacle avoidance pipeline for an autonomous vehicle in the CARLA simulation environment.
An Exploration of Fourier Features for Image and Video Representations
MATH 5466: Mathematics of Machine Learning and Data Analysis II (University of Minnesota)
We investigate the effectiveness of Fourier Features in MLPs for coordinate-based representations of images and videos. We also explore their theoretical motivations via the Neural Tangent Kernel (NTK).
