VASC Seminar
Gianfranco Doretto
Associate Professor
West Virginia University

Learning generative representations for image distributions

Abstract: Autoencoder neural networks are an unsupervised technique for learning representations, which have been used effectively in many data domains. While capable of generating data, autoencoders have been inferior to other models like Generative Adversarial Networks (GAN’s) in their ability to generate image data. We will describe a general autoencoder architecture that addresses this limitation, and [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Self-Supervising Occlusions for Vision

Abstract: Virtually every scene has occlusions. Even a scene with a single object exhibits self-occlusions - a camera can only view one side of an object (left or right, front or back), or part of the object is outside the field of view. More complex occlusions occur when one or more objects block part(s) of [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Development of an Agile and Dexterous Balancing Mobile Manipulator Robot

Abstract: This thesis focuses on designing and controlling a dynamically stable shape-accelerating dual-arm mobile manipulator, the Carnegie Mellon University (CMU) ballbot. The CMU ballbot is a human-sized dynamically stable mobile robot that balances on a single spherical wheel. We describe the development of a pair of seven-degree-of-freedom (DOF) humanoid arms. The new 7-DOF arm pair [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Massively Parallelized Lazy Planning Algorithms

GHC 4405

Abstract: Search-based planning algorithms enable autonomous agents like robots to come up with well-reasoned long horizon plans to achieve a given task objective. They do so by optimizing a task-specific cost function while respecting the constraints on either the agent (e.g. motion constraints) or the environment (e.g. obstacles). In robotics, such as in motion planning [...]

VASC Seminar
Daniel McDuff
Principal Researcher
Microsoft Research

Building Intelligent and Visceral Machines: From Sensing to Application

Abstract: Humans have evolved to have highly adaptive behaviors that help us survive and thrive. As AI prompts a move from computing interfaces that are explicit and procedural to those that are implicit and intelligent, we are presented with extraordinary opportunities. In this talk, I will argue that understanding affective and behavioral signals presents many opportunities [...]

VASC Seminar
Arun Mallya
Senior Research Scientist
NVIDIA

GANcraft – an unsupervised 3D neural method for world-to-world translation

Abstract: Advances in 2D image-to-image translation methods, such as SPADE/GauGAN, have enabled users to paint photorealistic images by drawing simple sketches similar to those created in Microsoft Paint. Despite these innovations, creating a realistic 3D scene remains a painstaking task, out of the reach of most people. It requires years of expertise, professional software, a library [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Run-Time Optimization in the Deep Learning Age

Abstract: In a recovery task one seeks to obtain an estimate of an unknown signal from a set of incomplete measurements. These problems arise in a number of computer vision applications, from image based tasks such as super-resolution and in-painting to 3D reconstruction tasks such as Non-Rigid Structure from Motion and scene flow estimation. Early [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

System Identification and Control of Multiagent Systems Through Interactions

GHC 6501

Abstract: This thesis investigates the problem of identifying dynamics models of individual agents of a multiagent system (MAS) and exploiting these models to shape their behavior using robots extrinsic to the MAS. While task-based control of a MAS using onboard controllers of its agents is well studied, we investigate (a) how easy it is for [...]

Faculty Events
Assistant Professor
Robotics Institute,
Carnegie Mellon University

Human-in-the-loop Model Creation

Newell-Simon Hall 4305

Abstract: Modern machine learning systems have made astonishing progress in automating labor-intensive tasks such as visual recognition and machine translation. While ML systems complete these tasks better and faster, humans are largely left behind. Indeed, most humans are entirely excluded from the creation process of machine learning models, except for tedious data annotation.   In [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Learning and Inference in Factor Graphs with Applications to Tactile Perception

Abstract: Factor graphs offer a flexible and powerful framework for solving large-scale, nonlinear inference problems as encountered in robot perception and control. Typically, these methods rely on handcrafted models that are efficient to optimize. However, robots often perceive the world through complex, high-dimensional sensor observations. For instance, consider a robot manipulating an object in hand [...]