Abstract: Mass-casualty triage robots must judge a victim’s ability to move—even when light is scarce, geometry is ambiguous, or some sensors fail. We tackle this challenge with a multimodal mesh-recovery framework that fuses RGB, LiDAR, and infrared (IR) inputs to reconstruct full-body SMPL meshes from whichever subset of sensors is available. Built on a frozen [...]

Abstract: With the advancement of AI and robotics, autonomous agents are becoming an essential part of our daily lives. Future large-scale multi-agent systems—such as those envisioned in smart cities—may involve tens of thousands of ground vehicles, aerial drones, and diverse robots, all requiring highly efficient coordination. This thesis focuses on a fundamental coordination problem underlying [...]

Abstract: Car-like robots are mechanically simple and dynamically stable, making them well-suited for applications such as inspections, surveillance, and search-and-rescue operations. Given a model of the robot's dynamics, model predictive control (MPC) plans optimal actions with specified constraints. The performance of MPC is limited by inaccuracies in the underlying system model - high fidelity models [...]

Abstract: Search and rescue missions demand rapid, reliable, and intelligent action in uncertain, often degraded environments. Autonomous unmanned aerial vehicles (UAVs) are increasingly well suited for these tasks, offering scalable coverage, high mobility, and the ability to reach otherwise inaccessible areas. However, to be effective in real-world search and rescue operations, UAVs must navigate safely [...]

Abstract:  This thesis explores motion planning and control strategies for enabling rapid quadrotor navigation in unknown environments using limited field-of-view depth sensors. We propose real-time onboard algorithms that enable agile flight through diverse and cluttered spaces.   First, we present a reactive planner based on forward-arc motion primitives that uses a short history of RGB-D [...]

Abstract:  With current population trends, concerns have been raised about the capability of global food networks for coming demand, especially in high-density urban centers. Alternatives to soil-based horticulture, such as controlled environment agriculture and hydroponics, have been developed and optimized for large-scale facilities. While large facilities can supply broad markets, small-scale ventures could support specific [...]

Abstract: Dynamic scene reconstruction is fundamental to real-time photorealistic rendering for applications such as virtual reality, telepresence, and robotic perception. Although early methods like Neural Radiance Fields (NeRFs) have achieved impressive results in novel view synthesis, their heavy computational demands severely hinder real-time deployment. This thesis presents four novel methods that collectively advance dynamic 3D [...]

Abstract: Prognostic modeling from medical data holds the promise of informing personalized care and improving clinical decision-making. This thesis explores two applications of survival analysis and deep learning to estimate long-term patient risk and treatment benefit in high-impact cardiopulmonary settings. In the first study, we develop a deep multimodal time-to-event prediction framework that estimates patient-specific [...]

Abstract: Many older adults, particularly those with Mild Cognitive Impairments (MCI) struggle with complex, sequential tasks such as meal preparation. In this thesis, we present a framework for personalized sequence prediction and assistance detection during meal preparation to support older adults, particularly those with Mild Cognitive Impairments (MCI). By leveraging the reasoning capabilities of large language [...]

Abstract: To enable robots to operate seamlessly in complex, real-world environments, they must master fine-grained manipulation skills and exhibit robust, adaptive behavior across diverse environments. This thesis explores a data-driven approach to learning generalizable and reactive manipulation policies by leveraging efficient data generation pipelines and expressive neural models. We first introduce BiDex, a low-cost teleoperation [...]

Abstract: Robots that operate in human-centric environments must integrate perception, reasoning, and action across multiple modalities to complete tasks according to user instructions. For these robots, being able to navigate according to a natural language instruction about the environment is an important capability, which requires 3D spatial reasoning, semantic scene understanding, and the ability to [...]