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

Physical systems operate in dynamic, uncertain, and constraint-heavy environments where classical reinforcement learning often struggles. In this talk, I present a hybrid framework where large language models act as a reasoning layer that guides an RL agent through high-level interpretation, constraint awareness, and adaptive strategy shaping. Instead of generating actions, the LLM provides structured contextual guidance that improves robustness, sample efficiency, and policy generalization in physical decision-making tasks. Early experiments demonstrate significant benefits under distribution shifts and safety-critical constraints that break standard RL. This work highlights a path toward more reliable, interpretable, and adaptable AI controllers for next-generation robotics and embodied systems.

World Models are emerging as the defining paradigm for the next decade of robotics and autonomous systems. Instead of depending on handcrafted perception stacks or rigid planning pipelines, modern world models learn a unified representation of an environment—geometry, dynamics, semantics, and agent behavior—and use that understanding to predict, plan, and act. This talk will break down why the field is shifting toward these holistic models, what new capabilities they unlock, and how they are already transforming AV and robotics research.

We then connect these advances to the Physical AI Workbench, a practical foundation for teams who want to build, validate, and iterate on world-model-driven pipelines. The Workbench standardizes data quality, reconstruction, and enrichment workflows so that teams can trust their sensor data, generate high-fidelity world representations, and feed consistent inputs into next-generation predictive and generative models. Together, world models and the Physical AI Workbench represent a new, more scalable path forward—one where robots and AVs can learn, simulate, and reason about the world through shared, high-quality physical context.

Data-centric workflows have driven major advances in computer vision, but they break down in physical, real-world robotic systems where data is costly, incomplete, and dominated by long-tail edge cases. In enterprise robotics, scaling labeled datasets alone is insufficient to achieve reliable perception, reasoning, and action under changing physical conditions. This talk examines how physics-informed foundation models incorporate world understanding and physical priors directly into vision and multimodal learning pipelines. By combining data with structure, constraints, and simulation on modern Physical AI stacks, robots can generalize more effectively, reduce data requirements, and operate with greater safety and reliability in deployment.

Model architectures get the papers, but data decides whether robots actually work. This talk introduces VLAs from a data-centric perspective: what makes robot datasets fundamentally different from image classification or video understanding, how the field is organizing its data (Open X-Embodiment, LeRobot, RLDS), and what evaluation benchmarks actually measure. We'll examine the unique challenges such as temporal structure, proprioceptive signals, and heterogeneity in embodiment, and discuss why addressing them matters more than the next architectural innovation.