ICONIP2025-SS-EN

Special Session “Imitation learning and latent models applicable to real-world robotics” @ ICONIP2025
November 20, 2025

Okinawa Institue of Science and Technology (OIST), Japan

Imitation learning and latent models applicable to real-world robotics

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.

Topic covered in the session:

  • Imitation learning methods applicable to robotics
  • Foundation model-based approrches to robotics
  • Scalable machine learning methods for real-world robotics
  • Real world robotics motivated by human/animal motor controls

Program (Tentative)

??:?? – ??:??
Eiji Uchibe (ATR)
Human-in-the-loop Generative Policy Learning from Demonstrations and Preferences.
??:?? – ??:??
Zhenyao Bi, Bolei Chen, and Ping Zhong
Sim-to-Real Reinforcement Learning for Hybrid Robotic System: Platform Design and Enhanced Hindsight Experience Replay.
??:?? – ??:??
Jia Li, Yinfeng Yu, Liejun Wang, Fuchun Sun, and Wendong Zheng
Audio-Guided Dynamic Modality Fusion with Stereo-Aware Attention for Audio-Visual Navigation.
??:?? – ??:??
Yuta Goto, Satoshi Yamamori, Satoshi Yagi, and Jun Morimoto
Parameter-Space Policy Composition for Sim-to-Real Transfer in Quadruped Locomotion Control.
??:?? – ??:??
Tetsuya Ogata (Waseda University) [Invited Talk]
TBA

366   Parameter-Space Policy Composition for Sim-to-Real Transfer in Quadruped Locomotion Control
487   Audio-Guided Dynamic Modality Fusion with Stereo-Aware Attention for Audio-Visual Navigation
910   Sim-to-Real Reinforcement Learning for Hybrid Robotic System: Platform Design and Enhanced Hindsight Experience Replay

Organizers

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