VASC Seminar
Wolfgang Heidrich
Professor of Computer Science and Electrical and Computer Engineering
KAUST Visual Computing Center

Learned Imaging Systems

Newell-Simon Hall 4305

Abstract: Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms. Of particular interest in recent years has been the development of end-to-end learned “Deep Optics” systems that use differentiable optical simulation in combination with backpropagation to simultaneously learn optical design and deep network post-processing for applications such as hyperspectral [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Accelerating Robot Task Learning with Large Pretrained Models and Internet Data

NSH 3305

Abstract: Large pre-trained models and internet data sources are key to general and efficient robot task learning. However, learning contact-rich behaviors, semantic task constraints, and robust task planning from internet data sources remains an open challenge. This proposal seeks to make progress towards a general robot task learning system leveraging pre-trained models and internet data. [...]

PhD Thesis Defense
PhD Student
Robotics Institute,
Carnegie Mellon University

A Modularized Approach to Vision-based Tactile Sensor Design Using Physics-based Rendering

NSH 4305

Abstract: Touch is an essential sensing modality for making autonomous robots more dexterous and allowing them to work collaboratively with humans. In particular, the advent of vision-based tactile sensors has resulted in efforts to design them for different robotic manipulation tasks. However, this design task remains a challenging problem. This is for two reasons: first, [...]

MSR Thesis Defense
MSR Student
Robotics Institute,
Carnegie Mellon University

Towards Universal Place Recognition

3305 Newell-Simon Hall

Title: Towards Universal Place Recognition Abstract: Place Recognition is essential for achieving robust robot localization. However, current state-of-art systems remain environment/domain-specific and fragile. By leveraging insights from vision foundation models, we present AnyLoc, a universal VPR solution that performs across diverse environments without retraining or fine-tuning, significantly outperforming supervised baselines. We further introduce MultiLoc, and enable [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Enhancing Model Performance and Interpretability with Causal Inference as a Feature Selection Algorithm

NSH 1305

Abstract: Causal inference focuses on uncovering cause-effect relationships from data, diverging from conventional machine learning which primarily relies on correlation analysis. By identifying these causal relationships, causal inference improves feature selection for predictive models, leading to predictions that are more accurate, interpretable, and robust. This approach proves especially effective with interventional data, such as randomized [...]

Seminar
Andy Kilianski
Program Manager, Health Science Futures
ARPA-H

ARPA-H and America’s Health: Pursuing High-Risk/High-Reward Research to Improve Health Outcomes for All

Newell-Simon Hall 4305

Dr. Andy Kilianski will provide an overview of ARPA-H, a new U.S. government funding agency pursuing R&D for health challenges. He will review the unique niche occupied by ARPA-H within the Department of Health and Human Services and how ARPA-H is already partnering with academia and industry to transform health outcomes across the country. Discussion [...]

MSR Thesis Defense
MSR Student
Robotics Institute,
Carnegie Mellon University

Scaling up Robot Skill Learning with Generative Simulation

Newell-Simon Hall 4305

Abstract:  Generalist robots need to learn a wide variety of skills to perform diverse tasks across multiple environments. Current robot training pipelines rely on humans to either provide kinesthetic demonstrations or program simulation environments with manually-designed reward functions for reinforcement learning. Such human involvement is an important bottleneck towards scaling up robot learning across diverse [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Unlocking Generalization for Robotics via Modularity and Scale

GHC 4405

Abstract: How can we build generalist robot systems? Looking at fields such as vision and language, the common theme has been large scale end-to-end learning with massive, curated datasets. In robotics, on the other hand, scale alone may not be enough due to the significant multimodality of robotics tasks, lack of easily accessible data and [...]