Student Talks

Abstract: Synthesizing photorealistic human faces from novel viewpoints using only a single frontal image remains a challenging problem in computer vision. Large viewpoint changes introduce geometric distortions, self-occlusions, and missing visual information, making identity preservation and high-frequency detail reconstruction particularly difficult. While recent generative approaches such as diffusion models and 3D-aware neural representations produce visually [...]

Abstract: Dynamic manipulation of deformable objects is challenging for humans and robots because they have infinite degrees of freedom and exhibit underactuated dynamics. This thesis introduces a Task-Level Iterative Learning Control method for dynamic manipulation of deformable objects and demonstrates this method on a non-planar rope manipulation task called the flying knot. Using a single human [...]

Abstract: Underwater robotics applications require accurate velocity sensing to enable long-term dead reckoning navigation in the absence of GPS or visual features. Velocity is typically measured with a Doppler Velocity Log, which measures the Doppler frequency shift induced by the motion of the sensor along four beams, from which the sensor velocity can be recovered. We expand upon [...]

Abstract: Visual-inertial state estimation integrates cameras and inertial measurement units (IMUs) to achieve accurate, metric-scale state estimation for autonomous systems. The covariance matrices associated with visual and inertial measurements determine how the estimator weights each sensing modality, making correct covariance modeling critical for fusion accuracy and consistency. However, most existing VI state estimators rely on [...]

Abstract:   Understanding real-world human-object interactions in images is an inherently many-to-many problem, where disentangling fine-grained and concurrent physical contacts is particularly challenging. Existing semantic contact estimation methods are either limited to single-human settings or require object geometry (e.g., meshes) in addition to the input image. Current state-of-the-art method leverages a powerful VLM for category-level [...]

Abstract: Zero-shot vision-language models such as CLIP have demonstrated remarkable recognition capabilities without task-specific training. However, their text embeddings are derived from large-scale pretraining over vast domains, which can introduce spurious correlations that hurt performance on minority groups when sensitive attributes are entangled with target classes. Correcting this bias is challenging in realistic settings, where [...]

Abstract: Rapid prototyping systems demand both adaptability to new components and precision in physical interaction. In this thesis, we explore how geometry can be used in two complementary ways: as a prompt for detecting novel industrial objects, and to support fine-grained perception in precision tasks. CAD-Prompted SAM3 enables instance segmentation directly from geometric specification, supporting [...]

Abstract: The MoonRanger Lunar rover will demonstrate continuous, on-board, long-range autonomous navigation at the Moon's South Pole. In this thesis, I describe how MoonRanger estimates its position and orientation using input from several sensors: Inertial Measurement Unit (IMU), Sun Sensor, wheel encoders, and cameras. I thoroughly review the state estimation approaches for prior space rovers [...]

Abstract: The evaluation of generalist robot policies, particularly Vision-Language-Action (VLA) models, is limited by the cost and scalability of real-world testing. This thesis introduces RobotArena ∞, a scalable benchmarking framework for large-scale simulation-based evaluation. Central to RobotArena ∞ is a fully automated reality-to-simulation (Real2Sim) pipeline that converts monocular video demonstrations from datasets such as Bridge, [...]

Abstract: Modern perception increasingly relies on wide field-of-view cameras, yet standard convolutional networks still operate on planar pixel grids designed for pinhole imagery. By Gauss's Theorema Egregium, no projection from the sphere to the plane preserves curvature, so every planar map of a spherical signal introduces spatially-varying distortion. Models trained on one lens therefore overfit [...]

Abstract: Synthetic data has emerged as a promising solution to the growing challenges of data acquisition in object detection. Modern detectors rely heavily on large-scale annotated datasets, yet collecting real-world data with high-quality labels is often costly, labor-intensive, and impractical in diverse or rare scenarios. By enabling controllable generation with automatic annotations, synthetic data provides [...]

Abstract: Recent progress in robotic foundation models has made broad, reusable robot competence increasingly plausible, but it has also brought a central challenge: how can robots continue to improve upon online deployment? This is especially acute in high-dimensional or contact-rich settings, where reactive, highly dexterous skills are critical but not trivial for BC priors. My [...]