München AI, Machine Learning and Computer Vision Meetup

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Bringing Specialist Agents to the Physical World to Improve Manufacturing Output

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.

About the Speaker

Clinton Smith is the co-founder and CEO of RIOS, whose AI agents watch, optimize and control production in various industrial facilities, including deep penetration into wood products and lumber. Clinton previously was a Senior Member of the Research Staff at Xerox PARC, leading multiple Dept. of Energy & Dept. of Defense projects, and holds a PhD in Electrical Engineering from Princeton University and a BS in Computer Engineering from Georgia Tech.

Accelerating Robotics with Simulation

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.

About the Speaker

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.

Anomalib 2.0: Edge Inference and Model Deployment

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.

About the Speaker

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.

Exploring Robotic Manipulation Datasets using FiftyOne: DROID and Amazon Armbench

About the Speaker

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.

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Towards Robotics Foundation Models that Can Reason

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.

About the Speaker

Jiafei Duan is a Ph.D. candidate in Computer Science & Engineering at the University of Washington, advised by Professors Dieter Fox and Ranjay Krishna. His research focuses on foundation models for robotics, with an emphasis on developing scalable data collection and generation methods, grounding vision-language models in robotic reasoning, and advancing robust generalization in robot learning. His work has been featured in MIT Technology Review, GreekWire, VentureBeat, and Business Wire.

Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection

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.

About the Speaker

Aimira Baitieva is a Research Engineer at Valeo, where she works primarily on computer vision problems. Her recent work has been focused on deep learning anomaly detection for automating visual inspection, incorporating both research and practical applications in the manufacturing sector.

The Digital Reasoning Thread in Manufacturing: Orchestrating Vision, Simulation, and Robotics

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.

About the Speaker

Vlad Larichev is an Industrial AI Lead at Accenture Industry X, specializing in applying AI, generative AI, and agentic AI to engineering, manufacturing, and large-scale industrial operations. With a background as an engineer, solution architect, and software developer, he has led AI initiatives across sectors including automotive, energy, and consumer goods, integrating advanced analytics, computer vision, and simulation into complex industrial environments.

Vlad is the creator of the Digital Reasoning Thread – a framework for connecting AI reasoning across visual intelligence, simulation, and physical execution. He is an active public speaker, podcast host, and community builder, sharing practical insights on scaling AI from pilot projects to enterprise-wide adoption.

The Road to Useful Robots

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.

About the Speaker

Michael Hart, also known as Mike Likes Robots. is a robotics software engineer and content creator. His mission is to share knowledge to accelerate robotics. @mikelikesrobots

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Are you working with computer vision in manufacturing and need deeper visibility into your datasets and models? Join us for a free 90-minute hands-on workshop and learn how to leverage the open-source FiftyOne toolset to optimize your visual AI workflows, from anomaly detection on the production line to worker safety and quality assurance in additive manufacturing.

In this session, you'll learn how to:

• Visualize and audit complex manufacturing datasets.
• Explore visual embeddings for failure mode analysis.
• Identify and fix labeling issues affecting production models.
• Perform advanced data curation for specialized use cases.
• Integrate with annotation tools, model pipelines, and plugins.

We'll take a data-centric approach to computer vision, starting with importing and exploring industrial visual data, including defects, wear patterns, and worker posture. You'll learn to query and filter datasets to surface edge cases, then use plugins and native integrations to streamline workflows.

We'll walk through generating candidate ground truth labels and evaluating fine-tuned foundational models — particularly relevant to manufacturers using pre-trained models for tasks like defect segmentation or object localization in dynamic environments.
By the end, you'll see how the FiftyOne App and SDK work together to enable more profound insight into visual AI systems. We'll conclude with a demo showcasing 3D view reconstruction for industrial inspection, revealing how Visual AI can bridge physical and digital layers of your production process.

Prerequisites: Basic knowledge of Python and computer vision fundamentals.

Resources Provided: All attendees will receive access to tutorials, videos, and the workshop codebase.

About the Instructor

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

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The Hidden Order of Intelligent Systems: Complexity, Autonomy, and the Future of AI

As artificial intelligence systems grow more autonomous and integrated into our world, they also become harder to predict, control, and fully understand. This talk explores how complexity theory can help us make sense of these challenges, by revealing the hidden patterns that drive collective behavior, adaptation, and resilience in intelligent systems. From emergent coordination among autonomous agents to nonlinear feedback in real-world deployments, we’ll explore how order arises from chaos, and what that means for the next generation of AI. Along the way, we’ll draw connections to neuroscience, agentic AI, and distributed systems that offer fresh insights into designing multi-faceted AI systems.

About the Speaker

Ria Cheruvu is a Senior Trustworthy AI Architect at NVIDIA. She holds a master’s degree in data science from Harvard and teaches data science and ethical AI across global platforms. Ria is passionate about uncovering the hidden dynamics that shape intelligent systems—from natural networks to artificial ones.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Building Agents That Learn: Managing Memory in AI Agents

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent's functionality. In this talk, we will explore these types of memory, discuss challenges with managing agentic memory, and present practical solutions for building agentic systems that can learn from their past executions and personalize their interactions over time.

About the Speaker

Apoorva Joshi is a Data Scientist turned Developer Advocate, with over 7 years of experience applying machine learning to problems in domains such as cybersecurity and mental health. As an AI Developer Advocate at MongoDB, she now helps developers be successful at building AI applications through written content and hands-on workshops.

Human-Centered AI: Soft Skills That Make Visual AI Work in Manufacturing

Visual AI systems can spot defects and optimize workflows—but it’s people who train, deploy, and trust the results. This session explores the often-overlooked soft skills that make Visual AI implementations successful: communication, cross-functional collaboration, documentation habits, and on-the-floor leadership. Sheena Yap Chan shares practical strategies to reduce resistance to AI tools, improve adoption rates, and build inclusive teams where operators, engineers, and executives align. Attendees will leave with actionable techniques to drive smoother, people-first AI rollouts in manufacturing environments.

About the Speaker

Sheena Yap Chan is a Wall Street Journal Bestselling Author, leadership speaker and consultant who helps organizations develop confidence, communication, and collaboration skills that drive innovation and team performance—especially in high-tech, high-change industries. She’s worked with leaders across engineering, operations, and manufacturing to align people with digital transformation goals.