Date: April 29, 2026
Speaker: Steve Heim, Senior Research Associate, Cornell Duffield Engineering
Title: Learning, Hierarchies, and Reduced Order Models
Abstract: With the advent of ever more powerful compute and learning pipelines that offer robust end-to-end performance, are hierarchical control frameworks with different levels of abstraction still useful? Hierarchical frameworks with reduced-order models (ROMs) have been commonplace in model-based control for robots, primarily to make long-horizon reasoning computationally tractable. I will discuss some of the other advantages of hierarchies, why we want ROMs and not simply latent spaces, and the importance of matching the time scale to each level of the hierarchy. In particular, I will show some results in learning for legged robots using ROMs with cyclic inductive bias, with both hand-designed and data-driven ROMs. I will also discuss using viability measures to estimate the intuitive notion of "how confident/safe is this action" and why this is only useful at the right level of abstraction.
Bio: Steve is faculty at Cornell University's department of Mechanical and Aerospace Engineering, where he uses robotics and machine learning to understand how and why animals move the way they do. Prior to Cornell, he spent time with the Biomimetic Robotics Lab at MIT, the Intelligent Control Systems group at the Max Planck Institute for Intelligent Systems (MPI-IS), completed his PhD also at MPI-IS with the Dynamic Locomotion Group, and obtained his MSc and BSc from ETH Zurich, with stays at EPFL, TU Delft, and Tohoku University.