PhD Thesis Defense
Nathan Brooks
Carnegie Mellon University

Situational Awareness and Mixed Initiative Markup for Human-Robot Team Plans

NSH 1305

Abstract: As robots become more reliable and user interfaces (UI) become more powerful, human-robot teams are being applied to more real world problems. Human-robot teams offer redundancy and heterogeneous capabilities desirable in scientific investigation, surveillance, disaster response, and search and rescue operations. Large teams are overwhelming for a human operator, so systems employ high level [...]

Field Robotics Center Seminar
Shobhit Srivastava

High-Fidelity Perceptual Representations via Hierarchical Gaussian Mixture Models

Event Location: NSH 1507Bio: Shobhit Srivastava is an M.S. student in the Robotics Institute at Carnegie Mellon University, advised by Prof. Nathan Michael. The primary focus of his research is to enable high-fidelity and efficient multimodal environment modeling on mobile autonomous systems to enable efficient inference with respect to the environment. He previously received his [...]

Field Robotics Center Seminar
Timothy Lee

State Estimation and Localization for ROV-Based Reactor Pressure Vessel Inspection Using a Pan-Tilt-Zoom Camera

Event Location: NSH 1305Bio: Timothy E. Lee is a M.S. in Robotics graduate student at Carnegie Mellon University, advised by Prof. Nathan Michael. Timothy's field robotics research seeks to enable robust, efficient, and autonomous inspection of critical infrastructure. Specifically, he is working towards improving the efficiency of nuclear power by enabling camera-based navigation of underwater [...]

Special Events

2017 Robotics Institute Administrative Support Staff Retreat

TBA

By Invitation Only - Please save the date and plan to join us for the Robotics Institute Staff Retreat to be held Friday, August 4, 2017. More information for the day meeting agenda will follow once it becomes available.

PhD Thesis Defense
Abhinav Shrivastava
Carnegie Mellon University

Discovering and Leveraging Visual Structure for Large-scale Recognition

GHC 8102

Abstract: Our visual world is extraordinarily varied and complex, but despite its richness, the space of visual data may not be that astronomically large. We live in a well-structured, predictable world, where cars almost always drive on roads, sky is always above the ground, and so on. As humans, the ability to learn this structure [...]