Calendar of Events
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Field Robotics Center Seminar
Abhinav Valada
Autonomous Intelligent Systems Lab, University of Freiburg
Learning Deep Multimodal Features for Reliable and Comprehensive Scene Understanding
Abstract Robust scene understanding is a critical and essential task for autonomous navigation. This problem is heavily influenced by changing environmental conditions that take place throughout the day and across seasons. In order to learn models that are impervious to these factors, mechanisms that intelligently fuse features from complementary modalities and spectra have to be [...]
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VASC Seminar
Byeong Keun Kang
UC San Diego
Scene Understanding
Abstract: Accurate and efficient scene understanding is a fundamental task in a variety of computer vision applications including autonomous driving, human-machine interaction, and robot navigation. Reducing computational complexity and memory use is important to minimize response time and power consumption for portable devices such as robots and virtual/augmented devices. Also, it is beneficial for vehicles [...]
1 event,
PhD Thesis Defense
Robot Design for Everyone: Computational Tools that Democratize the Design of Robots
Abstract: A grand vision in robotics is that of a future wherein robots are integrated in daily human life just as smart phones and computers are today. Such pervasive integration of robots would require faster design and manufacturing of robots that cater to individual needs. For instance, people would be able to obtain customized smart [...]
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2 events,
MSR Thesis Defense
Semantic Segmentation for Terrain Roughness Estimation Using Data Autolabeled with a Custom Roughness Metric
Traditional methods for off-road terrain estimation use some type of learning network to predict hand labeled classes of terrain such as short grass, tall grass, dirt, and trees. Other methods of learning which can give more detailed, but stilldiscrete classes, use on board sensors to measure the terrain roughness, and then predict the terrain type. There also exists [...]
PhD Thesis Defense
Robust Soft-Matter Robotic Materials
Abstract: Emerging applications in wearable computing, human-machine interaction, and soft robotics will increasingly rely on new soft-matter technologies. These soft-matter technologies are considered inherently safe as they are primarily composed of intrinsically soft materials---elastomers, gels, and fluids. These materials provide a method for creating soft-matter counterparts to traditionally rigid devices that exhibit the mechanical compliance [...]
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2 events,
MSR Thesis Defense
Automated Design of Manipulators For In-Hand Tasks
Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high level objectives as input. Our goal is to ultimately design a program that is able [...]
VASC Seminar
Shervin Ardeshir
University of Central Florida
Relating First-person and Third-person Videos
Abstract: Thanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones and glasses, we have access to a plethora of videos captured from the first person perspective. Capturing the world from the perspective of one's self, egocentric videos bear characteristics distinct from the more traditional third-person (exocentric) videos. In [...]
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1 event,
MSR Thesis Defense
Learning Neural Parsers with Deterministic Differentiable Imitation Learning
Abstract: In this work, we explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions of their input spaces, we formulate the problem of decomposing objects into segments [...]
1 event,
MSR Thesis Defense
Integrating Structure with Deep Reinforcement and Imitation Learning
Most deep reinforcement and imitation learning methods are data-driven and do not utilize the underlying structure of the problem. While these methods have achieved great success on many challenging tasks, several key problems such as generalization, data efficiency and compositionality remain open. Utilizing problem structure in the form of architecture design, priors, domain knowledge etc. may [...]
1 event,
MSR Thesis Defense
Learning Reactive Flight Control Policies: from LIDAR measurements to Actions
Abstract The end goal of a reactive flight control pipeline is to output control commands based on local sensor inputs. Classical state estimation and control algorithms break down this problem by first estimating the robot’s velocity and then computing a roll and pitch command based on that velocity. However, this approach is not robust in [...]
4 events,
MSR Thesis Defense
Transparency in Deep Reinforcement Learning Networks
In the recent years there has been a growing interest in the field of Explainability for machine learning models in general and deep learning in particular. This is because, deep learning based approaches have made tremendous progress in the field of computer vision, reinforcement learning, language related domains and are being increasingly used in application areas […]
MSR Thesis Defense
Geometric approaches to motion planning for two classes of low-Reynolds number swimmers
Microrobots have the potential to impact many areas of medicine such as microsurgery, targeted drug delivery and minimally invasive sensing. Just like microorganisms themselves, microrobots developed for these applications need to swim in a low-Reynolds number regime which warrants locomotive strategies that differ from their macroscopic counterparts. To this end, Purcell’s three-link planar swimmer has […]
MSR Thesis Defense
Autonomous 3D Reconstruction in Underwater Unstructured Scenes
Abstract Reconstruction of marine structures such as pilings underneath piers presents a plethora of interesting challenges. It is one of those tasks better suited to a robot due to harsh underwater environments. Underwater reconstruction typically involves human operators remotely controlling the robot to predetermined way-points based on some prior knowledge of the location and model […]
MSR Thesis Defense
Wire Detection, Reconstruction, and Avoidance for Unmanned Aerial Vehicles
Abstract Thin objects, such as wires and power lines are one of the most challenging obstacles to detect and avoid for UAVs, and are a cause of numerous accidents each year. This thesis makes contributions in three areas of this domain: wire segmentation, reconstruction, and avoidance. Pixelwise wire detection can be framed as a binary […]
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1 event,
PhD Speaking Qualifier
Multi-Robot Routing and Scheduling with Spatio-Temporal And Ordering Constraints
Abstract We consider the problem of allocation and routing a fleet of robots to service a given set of locations while minimizing makespan. The service start times for the locations are subject to AND/OR type precedence constraints. Spatio-temporal constraints prohibit certain states from all feasible schedules where a state is defined as a tuple of […]
1 event,
MSR Thesis Defense
The Art of Robotics: Toward a Holistic Approach
The Art of Robotics: Toward a Holistic Approach
I arrived at the Robotics Institute two years ago looking for a good project, something tangible and preferably related to legged locomotion. Instead, I met Matt Mason and started to think about the big picture, ask the big questions. What is manipulation? What is robotics? What makes robotics particularly hard? To answer these questions, I […]
1 event,
MSR Thesis Defense
Mapping gamma sources and their flux fields using non-directional flux measurements
There is a compelling need to determine the location and activity of radiation sources from the flux that they generate. There is also a need to create dense flux maps from sparse measurements. This research solves these dual problems. An example of a situation where these capabilities would be vital is at the location of […]
2 events,
MSR Thesis Defense
Automated Design of Special Purpose Dexterous Manipulators
Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this thesis, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high level objectives as input. Our goal is to ultimately design a program that is able […]
MSR Thesis Defense
Toward Invariant Visual Inertial State Estimation using Information Sparsification
Abstract In this work, we address two current challenges in real-time visual-inertial odometry (VIO) systems - efficiency and accuracy. To this end, we present a novel approach to tightly couple visual and inertial measurements in a fixed-lag VIO framework using information sparsification. To bound computational complexity, fixed-lag smoothers perform marginalization of variables but consequently deteriorate accuracy and […]
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