Seminar
Nano-optics for smart sensing and display
Abstract: Nano-optical devices provide a new way to control light at the subwavelength scale, enabling optical functionalities beyond conventional optics. By engineering the nanostructures, we can tailor the optical response as a function of space, polarization, wavelength, and angle of incidence -- effectively turning the optical front end into a controllable, programmable physical layer. This [...]
Generative Control, Action Chunking, and Moravec’s Paradox
Abstract: Moravec’s Paradox observes that AI systems have struggled far more with learning physical actions than symbolic reasoning. Yet just recently, there has been a tremendous increase in the capability of AI-driven robotic systems, reminiscent of the early improvements in language modeling capabilities a few years ago. In this talk, we provide mathematical evidence that learning in continuous-control [...]
Formal Methods for Robotics in the Age of Big Data
Abstract: Formal methods - mathematical techniques for describing systems, capturing requirements, and providing guarantees - have been used to synthesize robot control from high-level specification, and to verify robot behavior. Given the recent advances in robot learning and data-driven models, what role can, and should, formal methods play in advancing robotics? In this talk I [...]
Automatic Sampling for Discontinuities in Differentiable Shaders
Abstract: I will talk about a novel method for differentiating integrals of discontinuous functions, which frequently arise in inverse graphics, computer vision, and machine learning, and are a key bottleneck for gradient-based optimization. Prior approaches either rely on specialized routines to sample discontinuity boundaries of predetermined primitives, or use reparameterization techniques that suffer from high variance. [...]
Closest Point Geometry Processing
Abstract: Objects can be represented in various forms, including meshes, point clouds, parameterizations, and neural implicits. Traditionally, many algorithms are limited to a single specific representation. We focus on geometry processing with any representation supporting closest-point queries, making these methods universally applicable. Furthermore, objects can be manifold or nonmanifold, open or closed, orientable or not, and [...]
Physical Intelligence for Physical Care: Towards Stakeholder-Informed Caregiving Robots in the Real World
Abstract: How can we build robots that meaningfully assist people with mobility limitations in their daily lives? To support complex caregiving tasks such as robot-assisted feeding, bathing, transferring, and meal preparation, robots must physically interact with people and objects in dynamic, unstructured environments while maintaining safety. In this talk, I will present an overview of [...]
Video-informed Pose Spaces for Auto-Rigged Meshes
Abstract: Kinematic rigs make 3D meshes editable, but they do not specify which poses are plausible for a given asset. As a result, naively manipulating rig parameters can easily produce unrealistic deformations. Artists often address this by manually authoring pose spaces, but doing so requires substantial effort and expertise. In this talk, I will first give [...]
Title: Geometric Perspectives in AI
Abstract: Geometry can make AI approaches more accurate, efficient and controllable. In this talk, we cover three contributions that demonstrate this. The first is DeltaConv, a building block for CNNs on curved surfaces. DeltaConv works directly on the surface, rather than in 3D space. That means the networks can be more efficient and robust to deformations [...]
Generative Re-Photography with Video Models
Abstract: I will introduce "generative re-photography" methods that use new generative video models to get more out of your photos—even the blurry ones. First, I will present a method for converting motion-blurred images to video. This method can even predict the "past" and "future" (right before and after the capture) of a motion-blurred image. I will [...]