PhD Thesis Defense

Abstract:  To solve general manipulation tasks in real-world environments, robots must be able to perceive and condition their manipulation policies on the 3D world. These agents will need to understand various common-sense spatial/geometric concepts about manipulation tasks: that local geometry can suggest potential manipulation strategies; that changes in observation viewpoint shouldn't affect the interpretation of [...]

Abstract: This thesis aims to democratize time series intelligence by making advanced modeling capabilities accessible to users without specialized machine learning knowledge. We pursue this goal through three complementary contributions that build foundation models, improve our understanding of them, and address challenges emerging in their practical use. We start by introducing MOMENT, the first family [...]

Abstract: Achieving real-world robot safety requires more than avoiding risk—it demands embracing and managing it effectively. This thesis presents a safety case for risk-aware decision-making and behavior modeling in complex, multi-agent environments such as aviation and autonomous driving. We argue that safety arises from an agent’s ability to anticipate uncertainty, reason about intent, and act [...]

Abstract: Dexterous soft robot hands have the potential to transform how robots interact with the physical world by enabling safe and robust manipulation, even in unstructured environments. Due to their inherent compliance, soft hands could address many challenges ranging from caregiving and agriculture to precision manufacturing. However, despite this promise, dexterous soft hands have not [...]

Abstract: Since infancy, humans acquire motor skills, behavioral priors, and objectives by learning from their caregivers. Similarly, as we create humanoids in our own image, we aspire for them to learn from us and develop universal physical and cognitive capabilities that are comparable to, or even surpass, our own. In this thesis, we explore how [...]

Abstract: Real-world autonomy requires perception systems that deliver rich, accurate information given the task and environment. However, as robots scale to diverse and rapidly evolving settings, maintaining this level of performance becomes increasingly brittle and labor-intensive, requiring significant human engineering and retraining for even small changes in environment and problem definition. To overcome this bottleneck, [...]

Abstract: To enable robots to assist in everyday tasks in diverse natural environments such as homes, offices, and kitchens, it is critical to develop policies that generalize to novel tasks in unseen scenarios. Practical utility demands that these policies do not require task-specific adaptation at test time but can instead execute directly given a natural [...]

Abstract: Inverse rendering is the process of recovering the shape, materials, and lighting conditions of an environment from a set of images. Both this process as a whole and its individual components are fundamental to applications ranging from medical imaging to astronomy, and from AR/VR to embodied intelligence. In the thesis work discussed in this [...]

Abstract: In modern machine learning, the abundance of data generated across diverse and distributed sources has made distributed training a central paradigm, particularly in large-scale applications such as Federated Learning. However, two key challenges arise in distributed training: ensuring communication efficiency and preserving the privacy of sensitive data used during training. This thesis addresses these [...]

Abstract: Recent years have seen growing interest in developing robots capable of lifelong reliable operation in human-centric environments. Despite impressive recent progress towards long-horizon tasks such as laundry folding, current efforts are predominantly focused on quasi-static tasks in structured settings. General-purpose assistive robots should be capable of performing a wider range of dynamic and dexterous [...]