PhD Thesis Defense

Abstract:  Many real-world challenges, from robotic control to resource management, can be effectively formulated as optimization problems. Recent advancements have focused on incorporating these optimization problems as layers within deep learning pipelines, enabling the explicit inclusion of auxiliary constraints or cost functions, which is crucial for applications such as enforcing physical laws, ensuring safety constraints, [...]

Abstract: Multi-robot teams show exceptional promise across applications like Search-and-Rescue, disaster-response, agriculture, forestry, and scientific exploration due to their ability to go where humans cannot, parallelize activity, operate robustly to failures, and expand capabilities beyond that of an individual robot. Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for these multi-robot teams as [...]

Abstract: As humans, we are constantly interacting with and observing a three-dimensional dynamic world. Building this spatiotemporal or 4D understanding in vision algorithms is not straightforward as there is orders of magnitude less 4D data than 2D images and videos. This underscores the need to find meaningful ways to exploit 2D data to realize 4D [...]

Abstract: Robotic automation is generally welcomed for tasks that are dirty, dull, or dangerous, but with expanding robotic capabilities, robots are entering domains that are safe and enjoyable, such as creative industries. Although there is a widespread rejection of automation in creative fields, many people, from amateurs to professionals, would welcome supportive or collaborative creative [...]

Abstract: In mass casualty events and resource-constrained scenarios, limited responder capacity leads to preventable deaths. Time is of the essence particularly in severe trauma: the sooner individuals receive care, the higher their chances of survival. Yet a single responder can only manage a few patients simultaneously, leaving others unattended. This thesis addresses this capacity constraint [...]

Abstract: This thesis explores how explicit 3D geometric representations, trained at scale on synthetic data, can serve as priors to enhance robotic manipulation. Even with recent progress in geometric understanding, generalization to unseen objects and environments remains constrained by the scale and diversity of existing 3D training data. Although more large-scale 3D datasets have been [...]

Abstract: Recent advances in large-scale generative modeling have reshaped our understanding of visual intelligence. While models such as diffusion and autoregressive transformers have achieved remarkable success in image and video synthesis, their potential for visual perception and understanding remains underexplored. This thesis investigates how generative models can serve as powerful visual learners—bridging the long-standing divide [...]

Abstract:  Robots possess unique affordances granted by combining software and hardware. Most existing research focuses on the impact of these affordances on human-robot collaboration, but the theory of how robots can facilitate human-human collaboration is underdeveloped. Such a theory would be beneficial in education. An educational device can afford collaboration in both assembly and use. [...]

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Abstract: Although agriculture is a highly mechanized industry, essential and high-value subsectors such as horticulture and floriculture remain heavily reliant on manual labor because they require complex, contact-rich, and highly selective handling of both plants and produce. The variability and density of tree-canopy clutter further complicate the automation process, making robot performance difficult to quantify [...]