Join us for day two in a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI, Manufacturing and Robotics.

In this session, Steve Xie, CEO of Lightwheel, shares how simulation-first workflows and high-quality SimReady assets are transforming the development of visual AI in manufacturing. From warehouse anomaly detection to worker safety and object identification, Steve will explore how physics-accurate simulation and synthetic datasets can drive scalable AI training with minimal real-world data. Drawing from Lightwheel’s deployment of robot models like GR00T N1 in factory environments, the talk highlights how unifying vision, language, and action in simulation accelerates real-world deployment while improving safety, generalization, and efficiency.

Dr. Steve Xie is founder and CEO of Lightwheel, a company leading simulation infrastructure for embodied AI. Steve is a pioneer in generative-AI-powered simulation for robotics. He holds a B.S. from Peking University and a Ph.D. from Columbia University. Steve has led simulation efforts at NVIDIA and Cruise, where he built end-to-end synthetic data pipelines that set industry benchmarks for realism, scalability, and sim2real transfer.

Robotic manipulation research is rapidly advancing, and progress depends heavily on the accessibility of large, high-quality, diverse datasets. Two notable recent contributions include DROID, a large-scale, diverse dataset for robotic policy training, and Amazon ARMBench, a real-world benchmark for pick-and-place robots. In this lightning talk we will explore these complex, multimodal datasets with the open-source tool FiftyOne.

Allen Lee is currently a Machine Learning Engineer at Voxel51. Before that, Allen was the Co-Founder and Consulting Engineer at Leap Scientific LLC, where they provided scientific software consultancy services related to computation, machine learning, and computer vision.

When deploying models for inference, just exporting the models and calling them via the inferencers do not work. There are challenges related to pre-processing and post-processing. Any deviation in these steps during inference impacts performance. This talk is about how we re-designed components of Anomalib to integrate pre and post-processing steps in the model graph.

Samet Akcay is an AI Research Engineer at Intel who leads ML research and development efforts across multiple Open Edge Platform libraries, including Intel Geti, Datumaro, Anomalib, Training Extensions, and Vision Inference libraries. His research specializes in semi/self-supervised learning, zero/few-shot learning, and multi-modal object and anomaly detection. He is the creator of Anomalib, a major open-source anomaly detection library.

U.S. manufacturing productivity (output per labor hour) has been stagnant since 2008, driven by a stall in technology integration as well as available workers. RIOS Agents are collaborative AI perception and control systems that act as plant managers' eyes on the ground. Our Agents become specialists in a process, observing process steps, reporting on them, and ultimately controlling them by integrating into new or existing equipment. This enables factory production to be optimized in a way that was previously not possible.