Abstract: Off-road autonomous driving poses significant challenges such as navigating unmapped, variable terrain with uncertain and diverse dynamics. Addressing these challenges requires effective long-horizon planning and adaptable control. Model Predictive Control (MPC) methods rely on dense sampling and accurate dynamics models, making them computationally expensive and unsuitable for real-time long-horizon planning. In contrast, Reinforcement Learning [...]

Abstract: Dynamically stable mobile robots, like ballbots, are agile but highly sensitive to manipulator-induced disturbances. This feature makes loco-manipulation of dynamically stable mobile robots a tightly coupled whole-body control problem. This thesis investigates how manipulators can be leveraged not only for interacting with objects, but also for locomotion. First, we investigate effective wheelchair maneuvering using [...]

Abstract: As robots transition toward practical deployment as collaborative agents in human environments, it becomes essential to improve language-conditioned environmental understanding. A vision-and-language navigation (VLN) system must adapt to both the types of language used and the actions expected by a human collaborator. Often, a single sentence containing spatial relations and semantic attributes---e.g., “fetch the [...]

Abstract: Recent advances in robotic manipulation have established the effectiveness of learning from demonstration. However, while end-to-end policies excel in expressivity and flexibility for complex skill learning, they struggle to efficiently adapt to task variations in geometry and configuration. Conversely, alternative approaches relying on object-centric goal prediction offer improved generalizability, but rely on restrictive assumptions [...]

Abstract: Most digital content today remains inaccessible to people with disabilities, who make up 16% of the global population. A long-standing challenge in accessible computing is ensuring digital authors consistently provide the metadata required to make their content accessible through assistive technologies. Despite numerous specialized accessibility tools, authors often lack the time, training, or incentive [...]

Abstract: In this thesis we present REM, a framework for segmenting a wide range of concepts in video that can be described through natural language. Our method unlocks the universal visual-language mapping learned by video diffusion models on Internet-scale data by fine-tuning them on small-scale Referring Object Segmentation datasets. Our key insight is preserving the [...]

Abstract: In mass casualty situations, quickly identifying and prioritizing injuries can be the difference between life and death. Traditional triage systems rely heavily on trained human responders, but in complex or hazardous environments, human access may be delayed or limited. Robotic systems offer a promising solution to this problem by enabling remote, scalable, and fast [...]

Abstract: Many real-world decision-making problems are combinatorial in nature, where states can be seen as a combination of basic elements. Due to combinatorial complexity, observing all combinations of basic elements in the training set is infeasible, which leads to an essential yet understudied problem of zero-shot generalization to states that are unseen combinations of previously [...]

Abstract: Soft electrode arrays capable of conforming to complex anatomical surfaces are vital for emerging biomedical sensing technologies. They are particularly relevant in applications requiring large-area, high-resolution mapping. Reducing the time required for such mapping is critical to minimize overall procedure duration and improve clinical efficiency. However, the electrical performance of such systems under mechanical [...]

Abstract: With the growing deployment of robots across various safety-critical applications, ensuring safety has become increasingly essential to prevent accidents and enhance reliability. Addressing safety in robotic systems presents several inherent challenges. Firstly, in multi-agent settings, incorporating safety filters can adversely impact overall task performance, as agents must simultaneously ensure collision avoidance and timely task completion. To [...]

Abstract: Off-road autonomous driving presents a complex set of challenges, including navigation through unmapped environments, variable terrain geometries, and uncertain, non-stationary dynamics. These conditions demand planning and control strategies that are both long-horizon and adaptable. Traditional Model Predictive Control (MPC) methods rely on dense sampling and precise dynamics modeling, which limits their feasibility for real-time [...]

Abstract: Methods for 3D reconstruction from images or videos typically fall into two categories: 1) scene-specific test-time optimization (often via differentiable rendering) and 2) large-scale data-driven priors (often in the form of feedforward neural networks). This thesis presents two works that combine both paradigms for dynamic and large-scale 3D reconstruction from casual images and videos. [...]

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 [...]