Advances in AI at SDSU - October 14, 2026

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

This talk will introduces how a general-purpose agent can directly drive a physical robot throughout task execution without any task-specific or environment-specific training. We introduce Agent as Policy (AGP), which places task planning and execution under the agent’s control.

Physical AI systems such as robots, unmanned vehicles, and embodied agents must perceive and reason about a world that is dynamic, unstructured, and rarely matches their training distribution. Yet most multimodal models remain brittle when confronted with novel objects, unseen conditions, and a messy unstructured environment.

This talk develops how grounding perception across modalities and environments can make open-world learning more robust for physically situated systems. Drawing on our recent work, I will highlight the core challenges of multimodal open-world learning including out-of-distribution detection, distribution shift generalization, and cascaded semantic grounding and point toward multimodal systems that stay reliable when deployed in the unpredictable open world.

Long-range signal source detection in open-world environments presents a significant challenge, mainly due to the detrimental effects of the environment on signals propagation paths and presence of extraneous signal sources. While distributed static sensing systems are often employed, achieving scalability and comprehensive coverage across expansive areas is cost-prohibitive.

One affordable solution involves using low-cost autonomous unmanned vehicles (AUVs) that can leverage their mobility and actively explore the environment using extremum seeking control (ESC) algorithms. This talk presents novel ESC approaches to steer AUVs to sources of interest in an a priori unknown, highly non-convex map.