PhD Thesis Defense

Abstract:  Recent progress in robot learning has led to impressively capable manipulation systems. Much of the progress has come from scaling up human demonstrations; however, collecting such data through manual teleoperation is slow, costly, and hard to scale. Physics-based Simulation offers a scalable, safe, and efficient alternative for generating large demonstration datasets. However, some core [...]

Abstract: Navigation in unstructured environments is a capability critical to many robotics applications such as forestry, construction, disaster response and defense. In these domains, robots have the potential to eliminate much of the dull, dirty and/or dangerous work that is currently performed by humans. Unfortunately, these environments pose a unique set of challenges for navigation not [...]

Abstract: Robots that manipulate crops must contend with plants that occlude themselves, deform under contact, and make the manipulator contact structures it neither targets nor sees in advance. This thesis argues that autonomous manipulation of crops in unstructured agricultural environments is best advanced not by choosing between model-based and learned approaches, but by integrating them [...]

Abstract: Reliable state and geometry estimation from limited observations is a fundamental challenge in robotics and perception. Observations are often noisy, partial, or ambiguous, making estimation ill-posed without additional structure. This thesis argues that robust estimation in these regimes is enabled by integrating structured knowledge into the inference process. Estimation can be viewed as inferring [...]

Abstract: Mobile robots used for information gathering tasks rely on dense, predictive mapping of large-scale regions to determine where to take measurements. Current approaches to mapping commonly rely on Gaussian process regression to spatially correlate data, extrapolate from sparse samples, and estimate uncertainty. However, these approaches do not incorporate meaningful information about physical processes that [...]

Abstract: Generative visual AI has advanced by scaling data and compute, but its next bottleneck is oversight: the expert signals that evaluate, reward, and teach models what "good" looks like. Providing such oversight is increasingly difficult because foundation vision-language models now match or surpass most humans at the skills being judged. This thesis develops scalable [...]

Abstract: The alignment problem for robots considers how robots can learn to behave in accordance with human values. Today, robot learning paradigms enable humans to provide data (e.g., preference labels or demonstrations), which the robot uses to update its behavior (e.g., reward model or policy) to better align with human intentions.  However, the current paradigm requires [...]

Abstract Service robots – autonomous systems that perform personal and professional tasks – have become a common sight in the Global North. In Human-robot interaction (HRI), researchers rarely consider the design implications of service robots for people with disabilities (PwDs) beyond controlled assistive contexts, such as private homes and assisted living facilities. Nevertheless, the purview [...]

Date: June 26, 2026 Time: 10:00 – 11:30 AM Location: GHC – Room 4405 Zoom Link Type: Ph.D. Thesis Defense Who: Angela Chen Title: Behavioral Modeling of Interpersonal Dynamics as Controllable Agentic Systems: Empirically-grounded Adaptive Virtual Patients for Psychotherapy Training Abstract: The need for mental health care continues to outpace the supply of trained psychotherapists. Training is itself a bottleneck because supervision [...]

Abstract: Autonomous robot exploration and object search in unknown environments are fundamental capabilities in robotics, with applications ranging from search and rescue to structural inspection. A central challenge in both tasks is that robots often must make decisions based on information they have not yet directly observed–reasoning about unexplored space, predicting future information gain, or [...]