RI PhD Thesis Defense – Ruixuan Liu
Date: October 20, 2026 Time: 3:30–5:30 PM ET Type: RI PhD Thesis Defense Zoom Link Location:GHC 4405 Title: Prompt-to-Product: Towards Robots that Imagine, Reason, and Build Who: Ruixuan Liu Abstract: Recent advances in artificial intelligence (AI) and robotics have opened new possibilities for helping people turn ideas into physical assemblies. Large language models, multimodal foundation [...]
RI PhD Speaking Qualifier – Gabrielle Ohlson
Date: Wednesday, October 21 Time: 1 - 2 pm Room location: GHC 6115 Zoom link: https://cmu.zoom.us/j/97081285448?pwd=OvaBlpZG3whhrdtg3QSUP0bGPipbKW.1 Title: Designing & Evaluating Machine-Knitted Stim Objects for Autistic Adults Abstract: Stimming is a valuable coping strategy and form of expression for Autistic people. However, research has historically scrutinized stimming through a negative, corrective model. When research instead prioritizes the [...]
Simulation-Driven Robot Learning for the Lazy Researcher
Abstract: This talk will delve into the workflow of building robot learning methods powered by large-scale physics simulation. While the promise of simulation is low-effort data generation that scales cheaply with human effort, the reality is often not as simple, with considerable human effort required to actually make simulation a useful tool for real-world robotic [...]
RI PhD Thesis Proposal – Yilin Wu
Date: October 26, 2026 Time: 10:00-11:30 AM ET Type: RI PhD Thesis Proposal Zoom Link Location: NSH 4305 Title: Reconciling Adaptation and Generality in Robot Policies Who: Yilin Wu Abstract: Generalist robot policies—such as vision-language-action models (VLAs)—are trained end-to-end on large observation-action datasets and can perform increasingly diverse manipulation tasks. Yet these policies remain unreliable amid the complexity [...]
Controllable, Efficient and Personalized Generative AI: From Long-Horizon Video to Interactive World Models
Abstract: Generative models can produce compelling images and videos, yet building systems that people can reliably direct and personalize remains a fundamental challenge. These systems must support new forms of control, generate over extended horizons within practical computational budgets, and adapt to individual concepts and preferences. In this talk, I will explore these challenges through a [...]
RI PhD Thesis Proposal – Sriram Narayanan
Who: Sriram Narayanan Type: RI Thesis Proposal Date: Tuesday, October 27, 2026 Time: 02:00PM - 03:30PM (ET) Location: NSH 4305 Zoom link: Here Title: Thermal Computational Imaging through Light and Heat Transport Abstract: Computer vision seeks to understand the physical world from images, yet much of this understanding remains rooted in the appearance of reflected visible light. [...]
RI PhD Thesis Proposal – Chen Wu
Date: October 28, 2026 Time: 11:00 AM -12:30 PM ET Type: RI PhD Thesis Proposal Zoom Link Location: NSH 3305 Title: Learning to Explore and Learning from Exploration for Language Models Who: Chen Wu Abstract: Learning from experience offers a way for language models to improve beyond the solutions available in their training data. Instead of relying only on demonstrations, a [...]
Expanding What Robots Can Learn in the Real World
Abstract: Two robots can be equally capable today yet very different to teach tomorrow. As foundation models expand what robots can already do, an equally important question is how to expand their learning potential: the capabilities they can acquire next with the limited experience and supervision available to them. In this talk, I will argue [...]
RI PhD Thesis Proposal – Wenli Xiao
Date: November 2, 2026 Time: 12:30–2:00 PM EST Type: RI PhD Thesis Proposal Zoom Link Location: NSH 4305 Title: Scalable Robot Self-Improvement through Real-World Interaction Who: Wenli Xiao Abstract: A central ambition of AGI is to build agents that can continually acquire new capabilities from their own experience. For embodied agents, this means learning through [...]
Structured Learning for Fast Physical Prediction
Abstract: Physical simulation is essential for robotics and engineering design, but remains computationally expensive, especially for complex geometries and long time horizons. Learning-based approaches offer faster predictions, but often lack the mathematical structure needed for reliable simulation. In this talk, I will present three approaches that combine learning with mathematical structure: continuous reduced-order representations for [...]
Learning in Production: Building Physical AI at Walden Robotics
Abstract: Walden Robotics is a full-stack company building both the hardware and physical AI for general-purpose manufacturing robots. Walden was founded in January 2026 and deployed its first robot into production manufacturing two months later. It’s still there and the fleet has been learning on the job ever since. Our core conviction is that the [...]