{"id":2983,"date":"2025-09-09T00:11:55","date_gmt":"2025-09-08T15:11:55","guid":{"rendered":"https:\/\/bicr.atr.jp\/bri\/?page_id=2983"},"modified":"2025-09-12T14:06:04","modified_gmt":"2025-09-12T05:06:04","slug":"iconip2025-ss","status":"publish","type":"page","link":"https:\/\/bicr.atr.jp\/bri\/en\/iconip2025-ss\/","title":{"rendered":"ICONIP2025-SS"},"content":{"rendered":"

Special Session \u201cImitation learning and latent models applicable to real-world robotics\u201d @ ICONIP2025<\/a>
\nNovember 20, 2025
\n<\/span><\/h3>\n

Okinawa Institue of Science and Technology (OIST)<\/a>, Japan<\/p>\n

\n

Imitation learning and latent models applicable to real-world robotics<\/span><\/h4>\n

Imitation learning is a machine learning framework that enables learning agents to behave similarly to demonstrator agents. Recently, modern AI-based methods that include latent models have demonstrated great success in real-world applications. Among those, foundation model (FM)-based approaches equipped with latent models have also gained attention, offering the potential for broad applicability across a wide variety of robotic systems, architectures, and environments in a unified manner. However, simply applying FM-based methodology that has achieved great success in large language models to imitation learning by real-world robots is not trivial, given the relative scarcity of available data in the robotics domain. In this session, we will invite a leading researcher who has successfully applied imitation learning methodologies to real-world robotics and welcome several full-paper submissions as well as invited-paper submissions. This session aims to provide valuable insights into the future of imitation learning technologies, including FM-based approaches, in real-world robotics, and to explore the future direction of data-driven robotics.<\/p>\n

Topic covered in the session:<\/p>\n