PhD Thesis Defense

Abstract: Multi-view machine learning has garnered substantial attention in various applications over recent years. Many such applications involve learning on data obtained from multiple heterogeneous sources of information, for example, in multi-sensor systems such as self-driving cars, or monitoring intensive care patient vital signs at their bed-side. Learning models for such applications can often benefit [...]

Abstract: Human-in-the-loop control for mobile robots is an important aspect of robot operation, especially for navigation in unstructured environments or in the case of unexpected events. However, traditional paradigms of human-in-the-loop control have relied heavily on the human to provide precise and accurate control inputs to the robot, or reduced the role of the human [...]

Abstract: Modern planning methods are effective in computing feasible and optimal plans for robotic tasks when given access to accurate dynamical models. However, robots operating in the real world often face situations that cannot be modeled perfectly before execution. Thus, we only have access to simplified but potentially inaccurate models. This imperfect modeling can lead [...]

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 [...]

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 [...]

Abstract: Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans. In this thesis, we take a holistic approach to human behavior modeling and tackle its three essential aspects --- simulation, perception, and generation. Throughout the thesis, we show how the three aspects are deeply connected and [...]

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 [...]

Abstract: Most computer vision models in deployment today are not continually learning. Instead, they are in a “test” mode, where they will behave the same way perpetually, until they are replaced by newer models. This is a problem, because it means the models may perform poorly as soon as their “test” environment diverges from their [...]

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: [...]

Abstract: We have witnessed rapid advancement across major computer vision benchmarks over the past years. However, the top solutions' hidden computation cost prevents them from being practically deployable. For example, training large models until convergence may be prohibitively expensive in practice, and autonomous driving or augmented reality may require a reaction time that rivals that [...]