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 [...]

VASC Seminar
Bharath Hariharan
Assistant Professor
Cornell University

Learning to see from few labels

Abstract: Computer vision systems today exhibit a rich and accurate understanding of the visual world, but increasingly rely on learning on large labeled datasets to do so. This reliance on large labeled datasets is a problem especially when one considers difficult perception tasks, or novel domains where annotations might require effort or expertise. We thus [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Towards the Automated Design of Neural Networks

Abstract: Neural architecture search (NAS) is recently proposed to automate the process of designing network architectures. Instead of manually designing network architectures, NAS automatically finds the optimal architecture in a data-driven way. Despite its impressive progress, NAS is still far from being widely adopted as a common paradigm for architecture design in practice. This thesis [...]

RI Seminar
Alberto Rodriguez
Associate Professor
Mechanical Engineering, MIT

The Role of Manipulation Primitives in Building Dexterous Robotic Systems

Abstract: I will start this talk by illustrating four different perspectives that we as a community have embraced to study robotic manipulation: 1) controlling a simplified model of the mechanics of interaction with an object; 2) using haptic feedback such as force or tactile to control the interaction with an environment; 3) planning sequences or [...]

VASC Seminar
Adriana Romero-Soriano
Research Scientist
Facebook AI Research

Seeing the unseen: inferring unobserved information from multi-modal data

Abstract: As humans we can never fully observe the world around us and yet we are able to build remarkably useful models of it from our limited sensory data. Machine learning problems are often required to operate in a similar setup, that is the one of inferring unobserved information from the observed one. Partial observations [...]

PhD Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Analysis of Deadlock in Multirobot Systems

Abstract: Collision avoidance for multirobot systems is a well-studied problem. Recently, control barrier functions (CBFs) have been proposed for synthesizing controllers that guarantee safety while simultaneously encouraging goal stabilization for multiple robots. However, it has been noted that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that [...]