PhD Thesis Defense

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 [...]

Abstract: The physical interaction of aerial robots with their environment has countless potential applications and is an emerging area with many open challenges. Fully-actuated multirotors have been introduced to tackle some of these challenges. They provide complete control over position and orientation and eliminate the need for attaching a multi-DoF manipulation arm to the robot. [...]

Abstract: The advent of deep learning based artificial perception models has revolutionized the field of computer vision. These methods take advantage of the ever growing computational capacity of machines and the abundance of human-annotated data to build supervised learners for a wide-range of visual tasks. However, the reliance on human-annotated is also a bottleneck for [...]

Abstract: A robot that operates efficiently in a team with humans in an unstructured outdoor environment must translate commands into actions from a modality intuitive to its operator. The robot must be able to perceive the world as humans do so that the actions taken by the robot reflect the nuances of natural language and [...]

Abstract: The classical "modular and cascaded" autonomy stack (object detection, tracking, trajectory prediction, then planning and control) has been widely used for interactive autonomous systems such as self-driving cars due to its interpretability and fast development cycle. In this thesis, we advocate the use of such a modular stack but improve its accuracy and robustness [...]

Abstract: Teleoperated systems are used widely in deployed robots today, for such tasks as space exploration, disaster recovery, or assisted manipulation. However, teleoperated systems are difficult to control, especially when performing high-dimensional, contact-rich tasks like manipulation. One approach to ease teleoperated manipulation is shared control; this strategy combines the user's direct control input with an [...]

Abstract: This thesis contributes to the field of soft actuators by introducing a generalized framework of actuators from liquid metals. The evolution of robotic actuators has enabled robots to achieve a diversity of motions. Like natural muscles, which converts chemical energy into mechanical work in response to electrical stimuli from the nervous system, actuators are [...]

Abstract: Manipulation of deformable objects challenges common assumptions in robotic manipulation, such as low-dimension state representation, known dynamics, and minimal occlusion. Deformable objects have high intrinsic state representation, complex dynamics with high degrees of freedom, and severe self-occlusion. These properties make them difficult for state estimation and planning. In this thesis, we introduce benchmarks and [...]