Carnegie Mellon University
MSR Thesis Talk: Yutian Lei
Title: ARC: AdveRsarial Calibration between Modalities Abstract: Advances in computer vision and machine learning techniques have led to flourishing success in RGB-input perception tasks, which has also opened unbounded possibilities for non-RGB-input perception tasks, such as object detection from wireless signals, point clouds, and infrared light. However, compared to the matured development pipeline of RGB-input [...]
FRIDA: Supporting Artistic Communication in Real-World Image Synthesis Through Diverse Input Modalities
Abstract: FRIDA, a Framework and Robotics Initiative for Developing Arts, is a robot painting system designed to translate an artist's high-level intentions into real world paintings. FRIDA can paint from combinations of input images, text, style examples, sounds, and sketches. Planning is performed in a differentiable, simulated environment created using real data from the robot [...]
Perception for High-Speed Off-Road Driving
Abstract: On-road autonomous driving has seen rapid progress in recent years with driverless vehicles being tested in various cities worldwide. However, this progress is limited to cities with well-established infrastructure and has yet to transfer to off-road regimes with unstructured environments and few paved roads. Advances in high-speed and reliable autonomous off-road driving can unlock [...]
Continual Learning of Compositional Skills for Robust Robot Manipulation
Abstract: Real world robots need to continuously learn new manipulation tasks in a lifelong learning manner. These new tasks often share sub-structures (in the form of sub-tasks, controllers) with previously learned tasks. To utilize these shared sub-structures, we explore a compositional and object-centric approach to learn manipulation tasks. While compositionality in robot manipulation can manifest [...]
Junior Faculty PhD Admissions Process Presentation
A presentation lead by David Wettergreen regarding the PhD Admission process.
Carnegie Mellon University
MSR Thesis Talk: Samuel Ong
Title: Data-Driven Slip Model for Improved Localization and Path Following applied to Lunar Micro-Rovers Abstract Micro-lunar rovers need to solve a slew of challenges on the Moon, with no human intervention. One such challenge is the need to know their location in order to navigate and build maps. However, localization is challenging on the moon due [...]
Computational Interferometric Imaging
Abstract: Imaging systems typically accumulate photons that, as they travel from a light source to a camera, follow multiple different paths and interact with several scene objects. This multi-path accumulation process confounds the information that is available in captured images about the scene and makes using these images to infer properties of scene objects, such [...]
Making AI trustworthy and understandable by clinicians
Abstract: Understandable-AI techniques facilitate to use of AI as a tool by human experts, giving humans insight into how AI decisions are made thereby helping experts discern which AI predictions should or shouldn’t be trusted. Understandable techniques may be especially useful for applications with insufficient validation data for regulatory approval, for which human experts must remain the final decision [...]