Carnegie Mellon University
Towards Scalable Robot Learning: From Teleoperation to Web-scale Data
Abstract: Humanoid robots operating in human environments must manipulate articulated objects under contact and kinematic constraints that human demonstrations do not satisfy. That mismatch makes the human--humanoid embodiment gap the central bottleneck for learning from human data: robot demonstrations are expensive and sparse, while human demonstrations inhabit a different state-action space and often violate robot [...]
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 both perception and generation. This thesis studies consistency across views, time, and modalities by moving from dense grid-based representations toward entity-centric scene representations that can be maintained across frames and used for interactive generation. The first part of the [...]
Data Mining and Auto-Labeling for Promptable Driving Policies
Abstract: Autonomous vehicles (AVs) are being deployed at scale today, with companies like Waymo achieving upward of 500,000 passenger rides per week. Two of the largest remaining problems in the field are 1) building a system that generalizes across the long-tail of edge cases that are represented few or no times within the training data [...]
Tracing Generated Content Back to Training Data
Abstract: AI-generated content is inherently derived from training data, yet it remains a mystery which specific data points large generative models rely on for a given generation. To address this, my research focuses on training data attribution—identifying the training images that are most influential in synthesizing a specific output. The ideal objective is to find [...]