Neural Certificates for Safe Robotic System Planning and Control
Abstract: Achieving safety, scalability, and high performance in complex systems, such as multi-agent systems (MAS) control, is a central challenge in many real-world robotic deployments due to its computational complexity as a large-scale constrained optimal control problem. To address this, we introduce a novel graph control barrier function (GCBF) as a core tool for large-scale [...]
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 [...]
A Manipulation Journey
Abstract: The talk will revisit my career in manipulation research, focusing on projects that might offer some useful lessons for others. We will start with my beginnings at the MIT AI Lab and my MS thesis, which is still my most cited work, then continue with my arrival at CMU, a discussion with Allen Newell, [...]
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 [...]
Seeing Deep Inside Scattering Tissue Using Efficient, Noise-Robust Wavefront Shaping
Abstract: Scattering limits our ability to see inside biological tissue, as light penetration is severely distorted by tissue components with varying refractive indices. One promising method to overcome scattering aberration is wavefront shaping. This technique involves placing a spatial light modulator (SLM) in the microscope's optical path to correct the wavefront emitted from a point [...]
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 [...]