Abstract: This talk examines the physical interplay between light and heat: what is absent in the visible image—light not reflected by the scene—is absorbed as heat and manifests in the thermal spectrum. This process intrinsically couples illumination, material properties, and heat transport. By exploiting this coupling, a single visible–thermal image pair provides complementary information that enables [...]

Abstract: A robot that manipulates one object well may still fail on the next. Generalizing across diverse objects and tasks is hard because such objects vary widely in geometry, articulation, and interaction dynamics. Hierarchical policies offer a powerful approach: a high-level policy predicts sub-goal end-effector poses, and a low-level policy generates the actions to reach [...]

Abstract: Autonomous agents and robots are taking on increasingly collaborative roles alongside people, from self-driving vehicles that navigate roads alongside human drivers, to language-model agents that understand user intentions and execute tasks independently. In each of these settings, success is determined not only by an agent's individual task competence, but by its ability to work [...]

Abstract: Manual diagrams are a rich and common knowledge source humans use to learn new skills, but their use for robot learning is still underexplored. A challenge with instruction diagrams is that they communicate task progression in a sparse, qualitative visual format, relying on the learner's prior physical understanding to fill in unmentioned execution details. [...]

Abstract: World models have shown promise in robotics by imagining future outcomes of robot actions directly from high-dimensional sensor observations. However, learning visuomotor policies with world models still faces significant reliability challenges, as imagined futures from world models often diverge from the outcomes that may actually occur. This unreliability arises from uncertainty in learned world [...]

Abstract: World models have shown promise in robotics by imagining future outcomes of robot actions directly from high-dimensional sensor observations. However, learning visuomotor policies with world models still faces significant reliability challenges, as imagined futures from world models often diverge from the outcomes that may actually occur. This unreliability arises from uncertainty in learned world [...]

Abstract: High-speed autonomy in the real world requires accurate control, which often relies on dynamics models that capture the complex interaction between a robot and its environment. In off-road regimes, this terrain interaction dominates the robot’s dynamics, driven by formidable characteristics such as diverse surface properties, complex geometries, environment diversity, and high-speed instability. This thesis investigates how [...]

Abstract: United States apple growers lose more than $100 million a year to fire blight, a bacterial disease caused by Erwinia amylovora that is detrimental to pome fruit trees such as apple and pear.This bacterium infects blossoms, shoots, and branches during the bloom season causing tissue to die. Therefore, the detection and removal of infected tissues over [...]

Abstract: Learning robot manipulation policies typically requires large amounts of high-quality demonstration data, which is expensive to collect on real robots. While large robot and human datasets exist, embodiment gaps make transferring knowledge between platforms challenging. This thesis investigates cross-embodiment transfer—how robots can learn from experience collected on different robots and humans to reduce the [...]

Abstract: Modern language models’ in-context learning and instruction-following abilities make it possible to adapt agent behavior without updating model weights. This thesis studies online self-improvement, where an agent encounters each task only once and must accumulate reusable experience across a sequence of tasks, reflecting real-world deployments in which actions have persistent consequences and retries may [...]

Abstract:  Real-time robot motion in dynamic and uncertain environments requires algorithms that can make effective decisions under strict computational constraints. High-fidelity dynamics, large search spaces, uncertainty over future outcomes, and the pursuit of optimal motion each impose substantial computational costs. These challenges become especially pronounced in partially observable and rapidly changing environments, where a robot [...]

Abstract:   Retail and warehouse perception systems work against a catalog that never stops changing: a segmenter deployed today will eventually be asked to find products that did not exist when it was trained. A closed-set segmenter can only grow its vocabulary by widening its classification head and fine-tuning it, which risks the classes it [...]

Abstract: In wildfire scenarios, deploying autonomous drones requires safely anticipating the dynamic behavior of dense smoke to coordinate effectively. Unlike traditional rigid obstacles, smoke represents a fast-moving, complex fluid hazard that impairs visual navigation and onboard sensors. In this thesis, we propose a novel risk-aware, multi-agent path planning framework that treats dynamic smoke as a [...]

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 [...]

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 with complementarity constraints (QPCCs). While very expressive, QPCCs are non-convex problems, and few solvers exist for computing fast, local solutions [...]

Abstract: Autonomous robot navigation relies heavily on visual-inertial state estimation, which inherently accumulates drift over extended trajectories. Fusing Global Navigation Satellite System (GNSS) signals mitigates this drift, but evaluating these systems in hardware is resource-intensive. Therefore, existing works often rely on simplified, static noise models to generate GNSS measurements. This thesis presents a raytracing-based GPS [...]