Calendar of Events
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5 events,
MSR Thesis Defense
Monocular Depth Reconstruction using Geometry and Deep Networks
In this thesis, we explore methods of building dense depth map from monocular video. First, we introduce our multi-view stereo pipeline, which utilizes photometric bundle adjustment for getting accurate depth of textured regions from small motion video. Second, we improve the depth estimation of low-texture region by fusing deep convolutional network predictions. We categorize the […]
PhD Speaking Qualifier
Liquid Metal-Microelectronics Integration for a Sensorized Soft Robot Skin
Abstract: Progress in the emerging field of soft robotics depends on the integration of sensors that are capable of sensing, power regulation, and signal processing. Commercially available microelectronics are well suited for these needs, as well as small enough to preserve the natural mechanics of a host system. Here, we present a method for integrating […]
MSR Thesis Defense
Learning Depth from Monocular Videos using Direct Methods
The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community. Unsupervised strategies to learning are particularly appealing as they can utilize much larger and varied monocular video datasets during learning without the need for ground truth depth or stereo. In previous works, separate pose and […]
PhD Thesis Defense
Probabilistic Approaches for Pose Estimation
Abstract: Virtually all robotics and computer vision applications require some form of pose estimation; such as registration, structure from motion, sensor calibration, etc. This problem is challenging because it is highly nonlinear and nonconvex. A fundamental contribution of this thesis is the development of fast and accurate pose estimation by formulating in a parameter space […]
MSR Thesis Defense
Learning-based Lane Following and Changing Behaviors for Autonomous Vehicle
This thesis explores learning-based methods in generating human-like lane following and changing behaviors in on-road autonomous driving. We summarize our main contributions as: 1) derive an efficient vision-based end-to-end learning system for on-road driving; 2) propose a novel attention-based learning architecture with sub-action space to obtain lane changing behavior using a deep reinforcement learning algorithm; […]
2 events,
MSR Thesis Defense
Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
Abstract: In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale. More critically, all prior works fail to deal with the notorious [...]
MSR Thesis Defense
Reconstruction of dynamic vehicles from multiple unsynchronized cameras
Despite significant research in the area, reconstruction of multiple dynamic rigid objects (eg. vehicles) observed from wide-baseline, uncalibrated and unsynchronized cameras, remains hard. On one hand, feature tracking works well within each view but is hard to correspond across multiple cameras with limited overlap in fields of view or due to occlusions. On the other [...]
2 events,
PhD Thesis Defense
Algorithms for Timing and Sequencing Behaviors in Robotic Swarms
Abstract: Robotic swarms are multi-robot systems whose global behavior emerges from local interactions between individual robots and spatially proximal neighboring robots. Each robot can be programmed with several local control laws that can be activated depending on an operator's choice of global swarm behavior (e.g. flocking, aggregation, formation control, area coverage). In contrast to other [...]
PhD Thesis Defense
Data-Driven Statistical Models of Robotic Manipulation
Abstract: Improving robotic manipulation is critical for robots to be actively useful in real-world factories and homes. While some success has been shown in simulation and controlled environments, robots are slow, clumsy, and not general or robust enough when interacting with their environment. By contrast, humans effortlessly manipulate objects. One possible reason for this discrepancy [...]
0 events,
4 events,
PhD Thesis Proposal
Observing Humans In Their Natural Habitat: Data, Algorithms, and Analysis
Abstract: Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. First, we need to give computers insight [...]
MSR Thesis Defense
Ergodic Coverage and Active Search in Constrained Environments
In this thesis, we explore sampling-based trajectory optimization applied to search for objects of interest in constrained environments (e.g., a UAV searching for a target in the presence of obstacles). We consider two search scenarios: in the first scenario, accurate prior information distribution of the possible locations of the objects of interest is available, thus [...]
Special Events
The 2018 Robotics Institute Semi-formal
The 2018 Robotics Institute Semi-formal
Robotics Institute members and a guest are invited to join us for our annual semi-formal! Join us for an evening of music, fun, food and friends! Food and beverage will include: hot hors d'oeuvres, stations for carving, pasta, fruit, cheese, coffee and dessert and hosted non-alcoholic beverages. Cash bar. We will have a dj for [...]
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2 events,
PhD Thesis Proposal
Analysis of Spatio-Temporally Varying Features in Optical Coherence Tomographic (OCT) and Ultrasound (US) Image Sequences
Abstract: Optical Coherence Tomography (OCT) and Ultrasound (US) are non-ionizing and non-invasive imaging modalities that are clinically used to visualize anatomical structures in the body. OCT has been widely adopted in clinical practice due to its micron-scale resolution to visualize in-vivo structures of the eye. Ultra-High Frequency Ultrasound (UHFUS) can capture images at a depth [...]
PhD Thesis Proposal
Planning for Energy-Efficient Coverage and Exploratory Deviation by Robots in Rivers
Abstract: Manual collection of environmental data over a large area can be a time-consuming, costly, and even dangerous process, making it a perfect candidate for automation with mobile robots. Despite this clear suitability and numerous advances in robotics resulting in decreased costs, improved reliability, and increased ease of use, the problem of powering autonomous robots [...]
1 event,
PhD Thesis Defense
Learning to Learn for Small Sample Visual Recognition
Abstract: Understanding how humans and machines recognize novel visual concepts from few examples remains a fundamental challenge. Humans are remarkably able to grasp a new concept and make meaningful generalization from just few examples. By contrast, state-of-the-art machine learning techniques and visual recognition systems typically require thousands of training examples and often break down if […]
0 events,
1 event,
Field Robotics Center Seminar
Matthias Althoff
Cyber Physical Systems, Technische Universität München (TUM)
Composable Benchmarks for Safe Motion Planning on Roads
Abstract Numerical experiments for motion planning of road vehicles require numerous components: vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. Providing a description of the numerical experiment precise enough to reproduce it might require several pages of information. Thus, only key aspects are […]
0 events,
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1 event,
MSR Thesis Defense
Understanding Machine Vision through Human Vision
Abstract: Recent success in machine vision has been largely driven by advanced computer vision methods, most commonly known as deep learning based methods. While we have seen tremendous performance improvements in machine visual tasks, such as object categorization and segmentation, there remain two major issues in deep learning. Firstly, deep networks have been largely unable […]
1 event,
PhD Speaking Qualifier
Model Predictive Path Following for Wheeled Mobile Robots
Model Predictive Path Following for Wheeled Mobile Robots
Abstract: The navigation success of a wheeled mobile robotic mission is directly correlated to the degree of accuracy to which the robot can follow a given path. This, in turn, is largely affected by two factors: a) the environment and b) the intrinsic properties of the robot – its design, actuation mechanism etc. In the […]
2 events,
RI Event
Nick Nystrom
PSC
Accelerate Your Research with Resources for Scalable AI, Data, and Computing and Collaboration
Would your research program benefit from access to scalable AI resources at no cost, early access future hardware and software technologies, and complementary human expertise? To address such opportunities, PSC offers powerful resources for artificial intelligence, machine learning, general-purpose computing, and data management, together with AI, HPC, and domain expertise for collaboration and support. PSC’s […]
VASC Seminar
Saining Xie
Computer Science, UC San Diego
Deep Representation Learning with Induced Structural Priors
Abstract: With the support of big-data and big-compute, deep learning has reshaped the landscape of research and applications in artificial intelligence. Whilst traditional hand-guided feature engineering in many cases is simplified, the deep network architectures become increasingly more complex. A central question is if we can distill the minimal set of structural priors that can […]
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1 event,
PhD Speaking Qualifier
Generative Models of Orbital and In Situ Data for Autonomous Science
Abstract: The mapping and characterization of planetary bodies relies on the analysis of data collected by spacecraft and orbiters. For example, the instruments carried by the Mars Reconnaissance Orbiter have been crucial in the mapping of landforms, stratigraphy, minerals, and ice of Mars. These instruments provide extensive contextual information, but factors such as sparsity, resolution, […]
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2 events,
MSR Thesis Defense
Automated design, accessible fabrication, and learning-based control on cable-driven soft robots with complex shapes
The emerging field of soft robots has shown great potential to outperform their rigid counterparts due to the soft and safe nature and the capability of performing complex and compliant motions. Many are built, but the designs are conservative and limited to regular shapes. The widely-used fabrication method contains bulky pumps, tethered tubings, and silicone […]
VASC Seminar
Deepak Pathak
Computer Science at UC Berkeley
Lifelong Learning via Curiosity and Self-supervision
Abstract: Humans demonstrate remarkable ability to generalize their knowledge and skills to new unseen scenarios. One of the primary reasons is that they are continually learning by acting in the environment and adapting to novel circumstances. This is in sharp contrast to our current machine learning algorithms which are incredibly narrow in only performing the […]
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Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University