PhD Thesis Defense

Abstract: Real-world decision-making is rife with partial observability, long horizons, and complex multi-agent interactions. This thesis argues that abstraction—forming simplified representations of the task that retain relevant information—offers a unifying principle for tackling these challenges across model-free and model-based reinforcement learning (RL). We develop methods in which abstractions are not hand-designed but emerge from learning objectives, yielding representations that [...]

Abstract:  Humans are incredibly dexterous. We interact with and manipulate tools effortlessly, leveraging touch without a second thought. Yet, replicating this level of dexterity in robots is a major challenge. While the robotics community, recognizing the importance of touch in fine manipulation, has developed a wide variety of tactile sensors, how best to leverage these [...]

Abstract:  While image understanding, generation, and manipulation have matured rapidly in recent years, video remains challenging due to the significantly larger input size. As a result, tasks such as generating long videos or understanding extended video sequences remain out of reach for current models due to their computational cost. This talk presents a series of [...]

Abstract: Humans routinely rely on the sense of touch to better perceive the world. In environments characterized by poor lighting, occlusions, limited fields of view, or sparse visual features, contact feedback often becomes a primary source of information for perceiving the environment and successfully completing manipulation tasks. Everyday examples include locating a light switch in the [...]

Abstract: Safe autonomy in the real world requires more than safety in structured, low-dimensional settings. Robots deployed in everyday environments must cope with non-stationarity—objectives and dynamics that change due to human preferences or evolving operating conditions—and must also scale safety reasoning to high-dimensional robots and environments, where perception, dynamics, and safety constraints can be complex [...]

Abstract: Roadway congestion leads to wasted time and money and environmental damage. One possible solution is adding more roadway capacity, but this can be impractical especially in urban environments and still may not make up for a poorly-calibrated traffic signal schedule. As such, it is becoming increasingly important to use existing road networks more efficiently. [...]

Abstract: Takeo Kanade's famous quip - to infer geometry or motion from images, you must first know what in one image corresponds to what in another, has guided geometric vision for three decades. Deep learning seemed to bypass this: methods in 2017-2019 lifted 2D to 3D using only reprojection loss, exploiting an implicit bias toward smooth [...]

Abstract: Reliable trajectory forecasting is a foundational requirement for autonomous robotic systems operating in environments with humans. Despite substantial progress in modeling techniques, existing forecasting systems often fail under distribution shift, exhibit socially implausible behaviors, or report misleading performance due to limitations in data coverage and evaluation practices. This thesis argues that reliable trajectory forecasting [...]

Abstract : Small spacecraft are increasingly expected to perform complex missions despite strict constraints in mass, power, and onboard computation. Meeting these demands requires advances in autonomy that enable effective decision-making, adaptive control, and robust state estimation within resource-limited platforms. This thesis develops optimization- and machine-learning–based methods to improve spacecraft autonomy across guidance, navigation, and [...]

Abstract: Robot dexterity remains an open challenge in robotics that has the potential to transform manufacturing, healthcare, and daily life. Robots that safely and robustly interact with unstructured environments must combine compliant hardware with models and planners that tolerate uncertainty. Additionally, if robust robot dexterity is to be realized outside of research labs, it must [...]

Abstract Autonomous robots should maintain resilient spatial perception despite sensor degradation. Humans preserve spatial awareness when moving between well-lit and dark spaces or when vision is partially occluded. Neuroscience studies suggest this robustness relies in part on a proprioception-first sensory hierarchy: the vestibular system provides continuous inertial reference signals, while vision supplies corrective updates that [...]