RI Seminar
Stefanos Nikolaidis
Assistant Professor
Computer Science, University of Southern California

Towards Robust Human-Robot Interaction: A Quality Diversity Approach

Abstract: The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring the diverse scenarios of interaction between humans and robots in simulation can improve understanding of complex human-robot interaction systems and avoid potentially costly failures in real-world settings. [...]

VASC Seminar
Chao Chen
Assistant Professor
Stony Brook University

Topology-Driven Learning for Biomedical Imaging Informatics

Abstract: Thanks to decades of technology development, we are now able to visualize in high quality complex biomedical structures such as neurons, vessels, trabeculae and breast tissues. We need innovative methods to fully exploit these structures, which encode important information about underlying biological mechanisms. In this talk, we explain how topology, i.e., connected components, handles, loops, [...]

RI Seminar
Professor / Director of RI
Robotics Institute,
Carnegie Mellon University

Lessons from the Field: Deep Learning and Machine Perception for field robots

Abstract: Mobile robots now deliver vast amounts of sensor data from large unstructured environments. In attempting to process and interpret this data there are many unique challenges in bridging the gap between prerecorded data sets and the field. This talk will present recent work addressing the application of machine learning techniques to mobile robotic perception. [...]

VASC Seminar
Gianfranco Doretto
Associate Professor
West Virginia University

Learning generative representations for image distributions

Abstract: Autoencoder neural networks are an unsupervised technique for learning representations, which have been used effectively in many data domains. While capable of generating data, autoencoders have been inferior to other models like Generative Adversarial Networks (GAN’s) in their ability to generate image data. We will describe a general autoencoder architecture that addresses this limitation, and [...]

VASC Seminar
Daniel McDuff
Principal Researcher
Microsoft Research

Building Intelligent and Visceral Machines: From Sensing to Application

Abstract: Humans have evolved to have highly adaptive behaviors that help us survive and thrive. As AI prompts a move from computing interfaces that are explicit and procedural to those that are implicit and intelligent, we are presented with extraordinary opportunities. In this talk, I will argue that understanding affective and behavioral signals presents many opportunities [...]

VASC Seminar
Arun Mallya
Senior Research Scientist
NVIDIA

GANcraft – an unsupervised 3D neural method for world-to-world translation

Abstract: Advances in 2D image-to-image translation methods, such as SPADE/GauGAN, have enabled users to paint photorealistic images by drawing simple sketches similar to those created in Microsoft Paint. Despite these innovations, creating a realistic 3D scene remains a painstaking task, out of the reach of most people. It requires years of expertise, professional software, a library [...]

VASC Seminar
Deqing Sun
Senior Research Scientist
Google

Learning Optical Flow: Model, Data, and Applications

Abstract: Optical flow provides important information about the dynamic world and is of fundamental importance to many tasks. In this talk, I will present my work on different aspects of learning optical flow. I will start with the background and talk about PWC-Net, a compact and effective model built using classical principles for optical flow. Next, [...]

RI Seminar
Leila Bridgeman
Assistant Professor of Mechanical Engineering & Materials Science
Duke University

Distributed Dissipativity: Applying Foundational Stability Theory to Modern Networked Control

Abstract: Despite its diverse areas of application, the desire to optimize performance and guarantee acceptable behaviour in the face of inevitable uncertainty is pervasive throughout control theory. This creates a fundamental challenge since the necessity of robustly stable control schemes often favors conservative designs, while the desire to optimize performance typically demands the opposite. While [...]

RI Seminar
Assistant Professor
Robotics Institute,
Carnegie Mellon University

Haptic Perspective-taking from Vision and Force

Abstract: Physically collaborative robots present an opportunity to positively impact society across many domains. However, robots currently lack the ability to infer how their actions physically affect people. This is especially true for robotic caregiving tasks that involve manipulating deformable cloth around the human body, such as dressing and bathing assistance. In this talk, I [...]

VASC Seminar
Chen Sun
Assistant Professor, Computer Science
Brown University

Do Vision-Language Pretrained Models Learn Spatiotemporal Primitive Concepts?

Abstract:  Vision-language models pretrained on web-scale data have revolutionized deep learning in the last few years. They have demonstrated strong transfer learning performance on a wide range of tasks, even under the "zero-shot" setup, where text "prompts" serve as a natural interface for humans to specify a task, as opposed to collecting labeled data. These models are [...]