Abstract: The principles of scaling predict significant performance advantages for robots miniaturized to the millimeter and micrometer scales, including faster movements and higher precision. However, realizing these benefits is often hindered by manufacturing limitations and complex system-level interactions. This thesis investigates the effects of scaling on robot design for both manipulation and locomotion through the [...]

Abstract: The principles of scaling predict significant performance advantages for robots miniaturized to the millimeter and micrometer scales, including faster movements and higher precision. However, realizing these benefits is often hindered by manufacturing limitations and complex system-level interactions. This thesis investigates the effects of scaling on robot design for both manipulation and locomotion through the development, fabrication, and characterization of [...]

Abstract: We introduce Online-CBGT-Net, a neuromimetic architecture for interpretable, online prediction in streaming environments. Inspired by the cortico-basal ganglia-thalamic (CBGT) circuit in the mammalian brain, our model integrates evidence accumulation and a reset mechanism for continuous, online decision making. At each time step, the model receives input and incrementally accumulates evidence. A decision is made [...]

Abstract: There is a growing demand for mobile robots to act not only as passive observers but also to actively interact with their environment, especially in cluttered and social settings. Meeting this demand requires navigation systems that can both understand interaction-relevant properties of the environment and make plans accordingly. This thesis presents a modular approach [...]

Abstract: A common and controversial use of text-to-image models is to generate pictures by explicitly naming artists, such as “in the style of Greg Rutkowski”. Because the original prompt is usually unavailable, online platforms lack a reliable way to decide whether an uploaded image should be filtered for including artist names in its prompt---often required [...]

Abstract:  Sim2Real transfer, particularly for manipulation policies relying on RGB images, remains a critical challenge in robotics due to the significant domain shift between synthetic and real-world visual data. In this work, we propose SplatSim, a novel framework that leverages Gaussian Splatting as the primary rendering primitive to reduce the Sim2Real gap for RGB-based manipulation [...]

Abstract: Recent advances in Large Vision-Language Models (LVLMs) have led to impressive performance across a wide range of multimodal tasks. However, their tendency to produce hallucinated responses—text that is inconsistent with the visual input—poses a significant challenge to their reliability and real-world applicability. In this thesis, we investigate two training-free approaches for mitigating hallucinations during [...]

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 [...]