Tewodros W. Ayalew
PhD Student in Computer Science at the University of Chicago

Email: tewodrosayalew [at] uchicago [dot] edu
I am a PhD student in Computer Science at the University of Chicago, advised by Dr. Matthew Walter at the Robot Intelligence through Perception Laboratory (RIPL) at the Toyota Technological Institute at Chicago (TTIC). My research explores unsupervised representation learning for robotics and reinforcement learning from in-the-wild human videos. I am driven by the goal of building generalizable robot learning systems that go beyond curated datasets and controlled lab environments, enabling transfer across tasks and embodiments. By grounding learning in real-world visual data, I aim to bridge passive video understanding with active robotic control. Prior to my PhD, I earned an M.Sc. in Computer Science from the University of Saskatchewan, where I worked with Dr. Ian Stavness on unsupervised computer vision, and a B.Sc. in Software Engineering from Addis Ababa University.
news
Jun 26, 2025 | Our paper “PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement” is accepted at ICCV 2025! 🎉 😊 |
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Feb 10, 2025 | Our paper “Enabling End Users to Program Robots Using Reinforcement Learning” has been accepted at HRI 2025. |
Nov 26, 2024 | Our paper “PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement” is now available on Arxiv. |
Jun 15, 2021 | Our paper “Automatic Microplot Localization Using UAV Images and a Hierarchical Image-based Optimization Method” has been published in Plant Phenomics. |
Aug 28, 2020 | Our paper “Unsupervised Domain Adaptation for Plant Organ Counting” received the Best Paper Award in CVPPP at ECCV 2020! 🎉 😊 |
Aug 20, 2020 | Our paper “Unsupervised Domain Adaptation for Plant Organ Counting” has been accepted at ECCV 2020. |
Aug 15, 2020 | Our paper “AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images” has been accepted at ECCV 2020. |
selected publications
- PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement2025
- Unsupervised Domain Adaptation for Plant Organ CountingIn Proceedings of the European Conference on Computer Vision (ECCV), 2020