Abstract: This thesis advances 3D video understanding by bridging reconstruction and dynamics forecasting from monocular video, with applications in robotics, autonomy, and immersive environments. We introduce a novel pipeline that translates 2D video into 4D scenes by combining object-centric tracking, learned 2D view synthesis priors, and Gaussian splatting, enabling accurate geometry and motion recovery even [...]

Abstract: To build robot generalists, we need models that can operate across diverse tasks, scenes, and embodiments. While recent efforts scale data and model capacity and incorporate expressive generative objectives, most still rely on 2D inputs to predict inherently 3D actions—introducing a mismatch between perception and control. In my thesis, I explore how unifying 3D [...]

Abstract: Robots are increasingly deployed in settings where safety, performance, or both are mission-critical—from agile aerial vehicles avoiding collisions at high speed to manipulators executing intricate, contact-rich tasks. In my thesis, I present a unifying approach to these seemingly disparate challenges through the lens of reachable sets, a versatile but underutilized computational primitive in robotics. In [...]

Abstract: Human-centered computer vision technology relies heavily on large, diverse datasets, yet even the largest collections cannot fully capture the variability of human appearance, motion, and viewpoint. At the same time, collecting data from human subjects is time-consuming, labor-intensive, and raises privacy concerns. To overcome these challenges while maintaining efficiency, researchers increasingly turn to two [...]

Abstract: In recent years, we have seen how influential (and potentially harmful) algorithms can be in our lives through recommender systems and language models; sometimes creating polarization and conspiracies that lead to unsafe behavior. Now that robots are also growing more common in the real world, we must be very careful to ensure that AI-driven [...]