Student Talks
MSR Thesis Talk – Matt Martone
Title: Design and Control of a Large Modular Hexapod Abstract: Legged robotic systems have made great strides in recent years, but unlike wheeled robots, limbed locomotion does not scale well. Long legs demand huge torques, driving up actuator size and onboard battery mass. This relationship results in massive structures that lack the safety, portability, [...]
Carnegie Mellon University
Online Kinodynamic Planning for Teams of Aerial Robots in 3-D Workspaces
Abstract: An efficient online planning or replanning methodology is a critical requirement for scalable and responsive real world multi-robot deployments. The need to replan typically stems from the invalidation of existing plans due to incomplete knowledge of the environment, or, from scenarios that necessitate changing goal locations in response to evolving application requirements. In this [...]
Carnegie Mellon University
Expressive Real-time Intersection Scheduling: New Methods for Adaptive Traffic Signal Control
Abstract: Traffic congestion is a widespread problem throughout global metropolitan areas. In this thesis, we consider methods to optimize the performance of traffic signals to reduce congestion. We begin by presenting Expressive Real-time Intersection Scheduling (ERIS), a schedule-driven intersection control strategy that runs independently on each intersection in a traffic network. For each intersection, ERIS [...]
Carnegie Mellon University
Open-world 3D Object Detection
Abstract: Perception for autonomous robots presents a set of unique challenges: finding the right representation for 3D signals, adapting to an open-world setting, and exploiting geometric priors. Successfully detecting objects regardless of their labels lays a solid foundation for safe navigation. I will present two of my recent works in this line. First, I will [...]
MSR Thesis Talk: Jenny Nan
Title: Combining Deep Learning and Verification for Precise Object Instance Detection Abstract: Deep learning based object detectors often return false positives with very high confidence. Although they optimize generic detection performance, such as mean average precision (mAP), they are not designed for reliability. For a reliable detection system, if a high confidence detection is [...]
Carnegie Mellon University
MSR Thesis talk – Vasu Agrawal
Title: Ground Up Design of a Multi-modal Object Localization System Abstract: Rapid situational awareness is the key to enabling a successful response from first responders during an emergency, where time is of the essence. Emergency personnel are often sent into incident scenes to gather information, but this is often a dangerous and slow process. Subterranean environments [...]
MSR Thesis Talk – Swaminathan Gurumurthy
Title: Improving generalization in data-driven models with task-specific knowledge Abstract: With the rise of the over-parameterized deep learning models and massive datasets, many have started advocating towards minimizing the amount of prior knowledge added to a learning model. Ironically, the traditional machine learning community advocated for exactly the opposite. Whereas the latter assumes knowledge of [...]
MRSD Annual Poster Presentation
Nine RI MRSD program student teams will use posters, videos, and hardware to show their project work on truck crash avoidance, balance recovery, construction via UAVs, room tidying, crop disease detection, beach cleaning, temperature field sensing, UGV-UAV firefighting, and multirover moon pit modeling. Website: https://mrsd.ri.cmu.edu/project-examples/student-project-websites/spring-2019-fall-2019/
Adaptive Planning and Control of Wheeled Mobile Robots in Challenging Environments
Abstract: Over the last two decades, we have seen driverless cars conquer the Mojave desert, drive on mars and operate on our streets and warehouses. One of the most fundamental requirements of such robots is their ability to navigate their environment with minimal human oversight. As more robots graduate from the confines of laboratories to [...]
When to use CNNs for Inverse Problems in Vision
Abstract: Reconstruction tasks in computer vision aim fundamentally to recover an undetermined signal from a set of noisy measurements. Examples include super-resolution, image denoising, and non-rigid structure from motion\cite{Kong_2019}, all of which have seen recent advancements through deep learning. However, earlier work made extensive use of sparse signal reconstruction frameworks (e.g. convolutional sparse coding). While [...]