Student Talks
Customizing Text-to-Image Diffusion Models
Abstract: With the rapid advancement of generative models, their potential to transform creative content creation is increasingly evident. However, most large-scale generative models are primarily text-conditioned, given the availability of large-scale paired text–image datasets. In contrast, for most practical applications, creators often begin from an existing asset and wish to generate variations or modify it in [...]
Pushing the Frontier of Robotic Tool Manipulation by Treating the Hand and the Tool Together as a Machine
Abstract: Tool manipulation is an essential human skill. It expands our manipulation capability beyond the capability of the biological hand, and is a defining feature of many tasks centered on physical interaction with the real world. For humanoid robots to become general-purpose, they must master tool manipulation as well. However, the state-of-the-art humanoid robots equipped with [...]
Generative Robotics: Self-Supervised Learning for Human-Robot Collaborative Creation
Abstract: Robotic automation is generally welcomed for tasks that are dirty, dull, or dangerous, but with expanding robotic capabilities, robots are entering domains that are safe and enjoyable, such as creative industries. Although there is a widespread rejection of automation in creative fields, many people, from amateurs to professionals, would welcome supportive or collaborative creative [...]
Consistent Modeling of 4D Scenes for Perception and Generation
Abstract: A core challenge in vision is building representations that capture 3D scenes over time for perception and interactive generation. For accurate perception and plausible generation, we want consistency across views, time, and modalities. In this talk we explore consistency through the choice of representation, moving from dense grid formulations to entity-centric scenes that are easier to [...]
Embodied Artificial Intelligence for Emergency Care in Unstructured Environments
Abstract: In mass casualty events and resource-constrained scenarios, limited responder capacity leads to preventable deaths. Time is of the essence particularly in severe trauma: the sooner individuals receive care, the higher their chances of survival. Yet a single responder can only manage a few patients simultaneously, leaving others unattended. This thesis addresses this capacity constraint [...]
Unconstrained Perception for Scalable Robot Manipulation
Abstract: Advances in visual imitation learning driven by large-scale data and expressive policy architectures have yielded impressive progress on long-horizon, dexterous tasks. However, current success rates remain insufficient for industrial deployment, which demands near-perfect reliability on novel tasks. Compared to other fields such as NLP and CV, the available data in robotics is several orders [...]
Adaptive Robot Design for multimodal locomotion across diverse terrains
Abstract: Locomotion across natural environments such as sand, mud, and water presents a fundamental challenge for robots due to the heterogeneous, deformable, and often unpredictable properties of these substrates. In this talk, I will share how mechanical and structural adaptation can enable robust mobility in such complex settings through the development and characterization of two [...]
Title: Leveraging Geometric Priors for Robust Robotic Manipulation
Abstract: This thesis explores how explicit 3D geometric representations, trained at scale on synthetic data, can serve as priors to enhance robotic manipulation. Even with recent progress in geometric understanding, generalization to unseen objects and environments remains constrained by the scale and diversity of existing 3D training data. Although more large-scale 3D datasets have been [...]
KOROL: Learning Visualizable Object Feature with Koopman Operator Rollout for Manipulation
Abstract: Humans possess an extraordinary ability to manipulate objects, discerning position, shape, and other properties with just a glance. How can robots be endowed with similar perceptual and dexterous manipulation capabilities? In this talk, I will present a method that combines the sample efficiency of traditional model-based approaches with the high generalizability of deep learning [...]
3D Thermal Perception for Autonomous Navigation in Visually Degraded & Unstructured Environments
Abstract: Autonomous navigation in visually degraded and unstructured environments, such as darkness, smoke, and rough off-road terrain remains a significant challenge for current robotic systems. RGB cameras fail without illumination, and active sensors such as LiDAR degrade under aerosols and emit signals that are undesirable in sensitive or adversarial scenarios. In contrast, long-wave infrared (thermal) [...]