Events from January 20, 2017 – December 10, 2025 › Seminar › – Robotics Institute Carnegie Mellon University
2025-12-10T00:00:00-05:00
  • VASC Seminar
    Xun Huang
    Founder & CEO
    Stealth Startup

    From Video Generation to Video World Models

    3305 Newell-Simon Hall

    Abstract: Video diffusion models have achieved remarkable success in content creation, yet they still fall short of simulating interactive worlds that respond to users in real time. This talk examines the fundamental challenges preventing these models from evolving into true world simulators. I will present a series of works — CausVid, Self-Forcing, MotionStream, and State-Space [...]

    RI Seminar
    Jacob Andreas
    Associate Professor
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

    Just Asking Questions

    1403 Tepper School Building

    Abstract: In the age of deep networks, "learning" almost invariably means "learning from examples". We train language models with human-generated text and labeled preference pairs, image classifiers with large datasets of images, and robot policies with rollouts or demonstrations. When human learners acquire new concepts and skills, we often do so with richer supervision, especially [...]

    RI Seminar
    Assistant Professor
    Robotics Institute,
    Carnegie Mellon University

    How to Coordinate Thousands of Robots Efficiently and Robustly

    Abstract:  Large-scale robot fleets are increasingly deployed in warehouses, factories, transportation systems, and emerging robotics applications. Coordinating hundreds or thousands of robots in shared, cluttered spaces creates fundamental challenges in maintaining safety, preventing deadlocks, and minimizing congestion. In this talk, I will present our recent work on scalable imitation learning methods for coordinating 10k robots, automatic environment [...]

  • VASC Seminar
    Eliahu Horwitz
    Google PhD Fellow
    The Hebrew University of Jerusalem

    What Can We Learn from a Million Models?

    3305 Newell-Simon Hall

    Abstract: Machine learning has transformed many fields by learning from large collections of data. Yet, it is rarely applied to its own outputs: the models themselves. Today, with millions of publicly available models, a natural question arises: what can we do with so many models? In this talk, I will motivate two core applications that [...]

    VASC Seminar
    Simon Lucey
    Director
    The Australian Institute for Machine Learning

    Should we skip attention?

    3305 Newell-Simon Hall

    Abstract: Transformers are ubiquitous. They influence nearly every aspect of modern AI. However, the mechanics of their training remain poorly understood. This poses a problem for the field due to the immense amounts of data, computational power, and energy being invested in the training of these networks. I highlight a recent intriguing empirical result from [...]