AI x Robotics: Data, Infrastructure & Evaluation

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 show how physics simulation, Real2Sim scenes and cloud GPUs help evaluate imitation-learning policies before costly hardware runs.
For robotics and ML engineers, researchers, data infrastructure practitioners, and technical founders.
When: Wednesday, November 4, 2026, 18:00-21:00 JST.
Where: KERNEL HONGO, 3F, 4-1-4 Hongo, Bunkyo City, Tokyo 113-0033, Japan.
Language: English.
The running order may be updated as final speaker logistics 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)
19:00-19:30 - Talk 2: From Storage to Intelligence: The AI-Powered Future of Data (Hiroshi Hashimoto)
19:30-20:00 - Talk 3: Physics simulation for evaluating imitation-learning policies (Ryo Watanabe)
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.
Abstract: Robots generate data continuously and at massive scale, and the hard part is making sure 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