VASC Seminar
Ricardo Martin-Brualla
Researcher
Google

Photorealistic Reconstruction of Landmarks and People using Implicit Scene Representation

Abstract: Reconstructing scenes to synthesize novel views is a long standing problem in Computer Vision and Graphics. Recently, implicit scene representations have shown novel view synthesis results of unprecedented quality, like the ones of Neural Radiance Fields (NeRF), which use the weights of a multi-layer perceptron to model the volumetric density and color of a [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Bayesian Models for Science-Driven Robotic Exploration

Abstract: Planetary rovers have traversed many kilometers and made major scientific discoveries. However, they spend a considerable amount of time awaiting instructions from ground operators. The reason is that they are designed for automated science data collection, not for autonomous exploration. The exploration of more distant worlds with stronger communication constraints will require a new [...]

PhD Thesis Defense
Robotics Institute,
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 [...]

PhD Thesis Proposal
Project Scientist
Robotics Institute,
Carnegie Mellon University

Verification and Accreditation of Artificial Intelligence

Abstract: This work involves formally verifying a trained model's adherence to important design specifications for the purpose of model accreditation. Accreditation of a trained model requires enumeration of the explicit operational conditions under which the model is certified to meet all necessary specifications. By verifying model adherence to specifications set by developers, we increase the [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Direct-drive Hands: Making Robot Hands Transparent and Reactive to Contacts

Abstract: Industrial manipulators and end-effectors are a vital driver of the automation revolution. These robot hands, designed to reject disturbances with stiffness and strength, are inferior to their human counterparts. Human hands are dexterous and nimble effectors capable of a variety of interactions with the environment. Through this thesis we wish to answer a question: [...]

VASC Seminar
Guoliang Kang
Postdoctoral Research Associate
LTI, CMU

Towards Discriminative and Domain-Invariant Feature Learning

Abstract: Deep neural networks have achieved great success in various visual applications, when trained with large amounts of labeled in-domain data. However, the networks usually suffer from a heavy performance drop on the data whose distribution is quite different from the training one. Domain adaptation methods aim to deal with such performance gap caused by [...]

VASC Seminar
Zhiqiang Shen
Postdoctoral Researcher
Department of Electrical & Computer Engineering, CMU

Learning Efficient Visual Representation on Model, Data, Label and Beyond

Abstract: Efficient deep learning is a broad concept that we aim to learn compressed deep models and develop training algorithms to improve the efficiency of model representations, data and label utilization, etc. In recent years, deep neural networks have been recognized as one of the most effective techniques for many learning tasks, also, in the [...]

VASC Seminar
Yannis Kalantidis
Research Scientist
NAVER LABS Europe

Self-supervised Learning and Generalization

Abstract: Contrastive self-supervised learning is a highly effective way of learning representations that are useful for, i.e. generalise, to a wide range of downstream vision tasks and datasets. In the first part of the talk, I will present MoCHi, our recently published contrastive self-supervised learning approach (NeurIPS 2020) that is able to learn transferable representations [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Teleoperation via Intuition: Safe and Intent Oriented Navigation

Abstract: This thesis aims to enable seamless teleoperation of a mobile robot by a human operator, such that the robot navigates in unstructured environments following the operator’s intent intuitively, safely, and efficiently. The roles of the human and robot are disproportionate in traditional teleoperation: The human is responsible for most of the autonomy of the [...]

RI Seminar
Mac Schwager
Assistant Professor
Department of Aeronautics & Astronautics, Stanford University

Enabling Robots to Cooperate & Compete: Distributed Optimization & Game Theoretic Methods for Multiple Interacting Robots

Abstract: For robots to effectively operate in our world, they must master the skills of dynamic interaction.  Autonomous cars must safely negotiate their trajectories with other vehicles and pedestrians as they drive to their destinations.  UAVs must avoid collisions with other aircraft, as well as dynamic obstacles on the ground.  Disaster response robots must coordinate [...]