​Description

​How do we build the infrastructure that helps physical AI scale and improve? Join Tokyo AI for an evening exploring three complementary challenges: moving robot data reliably from fleets into training, building persistent data platforms for production AI, and evaluating learned robot policies before spending scarce time on real hardware.

​Petr Khrapchenkov (AIRoA) will share lessons from processing thousands of hours of robot data each month. Hiroshi Hashimoto (Wasabi Technologies Japan) will explore the storage foundations of production RAG, with an MCP demo. Ryo Watanabe (Muso Action) will explain how simulation-based and module-wise evaluation provide repeatable feedback and help pinpoint policy failures.

​For robotics and ML engineers, researchers, data infrastructure practitioners, and technical founders.

​The event title is tentative. Final speaker details and running order will be updated as they are confirmed.

​Agenda

​Provisional running order. Each 30-minute talk slot includes Q&A.

• ​18:00 - Doors open

• ​18:25-18:30 - Welcome

• ​18:30-19:00 - Talk 1: Challenges in scaling robotic data pipelines (Petr Khrapchenkov; working talk title)

• ​19:00-19:30 - Talk 2: From Storage to Intelligence: The AI-Powered Future of Data (Hiroshi Hashimoto)

• ​19:30-20:00 - Talk 3: Robot-policy evaluation (Ryo Watanabe; topic placeholder, final title to be announced)

• ​20:00-21:00 - Networking

• ​21:00 - Doors close

​Talks

​Talk 1: Challenges in scaling robotic data pipelines

​Speaker: Petr Khrapchenkov, Senior Research Engineer, AIRoA.

​Title status: Working talk title from the event plan; final title pending.

​Abstract: Robots generate data continuously and at massive scale, and the difficulty lies in ensuring that all of it reaches a training run. Every stage between recording and training is a point where data can be dropped, corrupted, or become too slow and expensive to use. As a fleet grows, these problems grow with it, and a pipeline that worked for a handful of robots stops working for hundreds.

​In this talk, we present the production pipeline we use to process thousands of hours of robot data every month. We follow the data end to end, from raw recordings through the data lake to training datasets flowing into the training pipeline, and focus on three areas. Ingress is about moving data off the robots reliably and without loss. Storage is about keeping large volumes of data durable, organized, and affordable. Query is about finding the right data and turning it into training datasets efficiently.

​For each area, we share the problems we faced, the design choices we made, and the lessons we learned. Along the way, we highlight how physical AI pipelines differ from regular data pipelines, and which of our decisions apply to other teams building similar systems.

​Bio: To be added.

​Talk 2: From Storage to Intelligence: The AI-Powered Future of Data

​Speaker: Hiroshi Hashimoto, Senior Solutions Architect, Wasabi Technologies Japan.

​Abstract: RAG helps AI models answer questions using a company's own data instead of relying only on what they were trained on. But most RAG discussions focus on embedding models and vector databases, while ignoring where the underlying data actually lives. This session looks at why a persistent data platform like Wasabi enables production RAG applications to keep improving without rebuilding their data from scratch. It will also contain a simple demo of managing the platform using MCP.

​Bio: Hiroshi Hashimoto is a Solutions Architect at Wasabi specializing in cloud storage-centric data infrastructure. A former pre-sales engineer for cloud storage, DaaS, and virtualization vendors, he brings deep multi-vendor expertise in designing flexible hybrid cloud solutions.

​Talk 3: Robot-policy evaluation

​Speaker: Ryo Watanabe, Muso Action.

​Title status: Topic placeholder; final talk title to be announced.

​Abstract: Evaluating learned robot policies on real hardware is slow, expensive, and hard to reproduce. In this talk, I'll share how we approach policy evaluation at scale: (1) simulator-based evaluation pipelines that give fast, repeatable feedback before any robot time is spent, and (2) module-wise evaluation that decomposes a policy into its components, vision encoder, action expert, and so on, to localize where failures actually come from.

​Speaker background: To be added.

​Organizers

​Ilya Kulyatin is an entrepreneur with work and academic experience in the US, Netherlands, Singapore, UK, and Japan. He holds a BA in Economics, an MA in Finance, and an MSc in Machine Learning. He's a 3x founder, now helping Japan grow the local AI ecosystem through a not-for-profit community, Tokyo AI (TAI), while building an AI-native system integrator and solutions provider, Foundry Labs株式会社.

​Supporters

​Foundry Labs K.K. is a Tokyo-based AI systems integrator and solutions provider, delivering end-to-end support for enterprises: from strategy design through implementation, deployment, and operations. They tailor AI to each client's operational, regulatory, and security requirements, with hands-on experience across finance, government, and industry, and a track record of shipping production systems in secure and regulated environments.

​About TAI

​Tokyo AI (TAI) brings together engineers, researchers, investors, product managers, and corporate innovation leaders building AI in Japan and connecting with the global ecosystem.

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