Student Talks

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 [...]