Learning From History: Test-Time Verification and Adaptation for Robotics
Abstract: The physical properties and dynamics that decide how an object or environment responds to a robot's actions are often impossible to determine from visual observation alone. An object's mass distribution and friction, the kinematics of an articulated object: these latent factors dictate the correct action, yet they leave little or no trace in a single [...]
View Generalizable Manipulation Policies via Sim-to-Real Transfer
Abstract: Visual imitation learning is a promising approach to training robot manipulation policies capable of completing a wide variety of tasks. A key requirement for these manipulation policies is to exhibit robust generalization capabilities when deployed in the real world, where the objects, scenes, and sensors a robot encounters differ from those seen during training. In [...]
Learning-Guided Search over Continuous Actions for Long-Horizon Robot Manipulation
Abstract: Despite recent advances in policy learning, long-horizon manipulation remains difficult because learned policies must avoid compounding errors while preserving future feasibility. While search-based planning can explicitly reason over future consequences, it becomes expensive in high-dimensional continuous action spaces. Classical Task and Motion Planning methods address this by introducing symbolic objects, relations, and abstractions for [...]
Exploring High-Level Goal Prediction for Hierarchical Imitation Learning in Robotic Manipulation
Abstract: Hierarchical imitation learning has become an effective approach for robotic manipulation: a high-level policy predicts a sub-goal end-effector pose, while a low-level policy executes the actions needed to reach it. This decomposition improves generalization and provides an interpretable interface, but the design of the high-level goal predictor remains an open question. This thesis studies [...]
Beyond Vision-Language-Action Models: Adapting, Steering, and Accelerating Generalist Robot Policies
Abstract: Generalist robot policies, vision-language-action models that combine a large pretrained vision-language model backbone with a diffusion or flow-matching action head, are increasingly capable, yet hard to deploy in the real world. Three gaps separate such a policy from a deployable one: a data gap (adapting to a new task still demands task-specific teleoperation data), an inference gap (the policy [...]
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 [...]
Toward Curiosity-Driven Embodied Learning Through World Models
Abstract: Curiosity allows animals and humans to learn through interaction without explicit instruction. This thesis asks how principles of natural curiosity can be translated into embodied agents, and what kinds of world models are needed as bodies, action spaces, and environments become more complex. We study this progression in simulation, using animal behavior and neural dynamics [...]
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 [...]
MapForest: A Modular Field Robotics System for Forest Mapping
Abstract: Forests present compounding challenges for mobile mapping systems. Dense canopy degrades GNSS, uneven terrain demands deployment across diverse platforms, and no single sensing platform can capture the full vertical structure of a forest — from the canopy above to the understory below. Yet precise, georeferenced maps of individual trees are exactly what ecologists and [...]
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 [...]
Cutting the Skip: Training Residual-Free Transformers
Abstract: Transformers are ubiquitous. They influence nearly every aspect of modern AI. However, the mechanics of their training remain poorly understood. This poses a problem for the field due to the immense amounts of data, computational power, and energy being invested in the training of these networks. I highlight a recent intriguing empirical result from [...]
[MS Thesis Talk] Terrain-Aware Dynamics Models for High-Speed Off-Road Navigation
Date: Tuesday, July 28, 2026 Time: 1:30pm- 2:30pm Location: GHC 9115 Title: Terrain-Aware Dynamics Models for High-Speed Off-Road Navigation Committee: Wenshan Wang (Co-Research Advisor) Sebastian Scherer (Co-Research Advisor) Aaron Johnson Anoushka Alavilli
[MS Thesis Talk] Marble: An On-Manifold Approach to Solving Mathematical Programs with Complementarity Constraints
Date: Thursday, July 30, 2026 Time: 3:30 PM - 4:30 PM Location / ZOOM Link: (GHC 6115 / https://cmu.zoom.us/j/96096959582 ) Abstract: Many problems in robotics require reasoning over a mix of continuous dynamics and discrete events, such as making and breaking contact in manipulation and locomotion. These problems are locally well modeled by quadratic programs [...]