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

In recent years, we have witnessed remarkable progress in generative AI, particularly in language and visual understanding and generation. This leap has been fueled by unprecedentedly large image–text datasets and the scaling of large language and vision models trained on them. Increasingly, these advances are being leveraged to equip and empower robots with open-world visual understanding and reasoning capabilities.

Yet, despite these advances, scaling such models for robotics remains challenging due to the scarcity of large-scale, high-quality robot interaction data, limiting their ability to generalize and truly reason about actions in the real world. Nonetheless, promising results are emerging from using multimodal large language models (MLLMs) as the backbone of robotic systems, especially in enabling the acquisition of low-level skills required for robust deployment in everyday household settings.

In this talk, I will present three recent works that aim to bridge the gap between rich semantic world knowledge in MLLMs and actionable robot control. I will begin with AHA, a vision-language model that reasons about failures in robotic manipulation and improves the robustness of existing systems. Building on this, I will introduce SAM2Act, a 3D generalist robotic model with a memory-centric architecture capable of performing high-precision manipulation tasks while retaining and reasoning over past observations. Finally, I will present MolmoAct, AI2’s flagship robotic foundation model for action reasoning, designed as a generalist system that can be post-trained for a wide range of downstream manipulation tasks.

In this talk, I will share our recent research efforts in visual industrial anomaly detection. It will present a comprehensive empirical analysis with a focus on real-world applications, demonstrating that recent SOTA methods perform worse than methods from 2021 when evaluated on a variety of datasets. We will also investigate how different practical aspects, such as input size, distribution shift, data contamination, and having a validation set, affect the results.

Manufacturing is entering a new phase where AI is no longer confined to isolated tasks like defect detection or predictive maintenance. Advances in reasoning AI, simulation, and robotics are converging to create end-to-end systems that can perceive, decide, and act – in both digital and physical environments.

This talk introduces the Digital Reasoning Thread – a consistent layer of AI reasoning that runs through every stage of manufacturing, connecting visual intelligence, digital twins, simulation environments, and robotic execution. By linking perception with advanced reasoning and action, this approach enables faster, higher-quality decisions across the entire value chain.

We will explore real-world examples of applying reasoning AI in industrial settings, combining simulation-driven analysis, orchestration frameworks, and the foundations needed for robotic execution in the physical world. Along the way, we will examine the key technical building blocks – from data pipelines and interoperability standards to agentic AI architectures – that make this level of integration possible.

Attendees will gain a clear understanding of how to bridge AI-driven perception with simulation and robotics, and what it takes to move from isolated pilots to orchestrated, autonomous manufacturing systems.

This talk explores the current state of AI-enabled robots and the issues with deploying more advanced models on constrained hardware, including limited compute and power budgets. It then moves on to what's next for developing useful, intelligent robots.