Dear RoboCup Community,
we are excited to invite you to submit to our upcoming Research Topic on **Leveraging Foundation Models for Interactive and Adaptive Robot Policy Learning** in Frontiers in Robotics and AI (FRAI) – a leading journal in the field of Robotics and AI.
This topic is especially interesting for those among you that work in fields related to human-robot interaction, such as the @Home or Industrial Leagues.
*Website* https://www.frontiersin.org/research-topics/71044/leveraging-foundation-mode...
If you are interested in submitting, please klick "Participate in this topic" on the website and we will make sure to keep you updated and remind you to submit your research summary in time.
*Description* This Research Topic aims to investigate the transformative potential of foundation models (FMs) for effective and adaptive robot-policy learning. We seek to highlight cutting-edge research and innovative applications of these models, showcasing novel adaptation algorithms and human-robot interaction and collaboration strategies. Our focus is on how robots, empowered by off-the-shelf and/or domain-adapted foundation models, can perceive, reason, interact, and make intelligent decisions in open-world domains. By enabling effective and generalizable perception, reasoning, and multi-turn human-robot interactions, we aim to allow robots to acquire knowledge actively from external environments and humans in a targeted manner, interpret and reason about information in relation to its multi-modal context, facilitating new policy adaptation, refining existing policies, and enhancing its task and motion planning ability.
*Topics of Interest* We invite researchers and practitioners to submit original research, review articles, case studies, and technical notes that explore, but are NOT limited to, the following areas: - Applications of off-the-shelf Foundation Models for Robot Policy Learning - Embodied Multi-modal Vision and Language models - Efficient Domain Adaptation of Foundation Models for Policy Learning - Interactive Robot Policy Learning and Grounding - Human-robot Collaboration for Policy Learning - Policy Learning with Few-shot Demonstrations - Data Augmentation using Foundation Models - Interactive Reasoning and Task Planning with Foundation Models - Integrated Planning and Foundation Models - Applications and Fine-tuning of Foundation Models for Task and Motion Planning - Learning Safe Policy through Human-Robot Interaction - Trustworthy AI and AI Safety - In-context Learning (ICL) for Decision-Making - Knowledge Representation and Reasoning for Agents - Interactive Open-Vocabulary Robot Navigation and Manipulation - Policy Correction and Adaption through Human-robot Interaction - Policy Evaluation using Foundation Models - Applications and Fine-tuning of Foundation Models for Spoken Dialogue - Detecting and Adapting to Novelty in Open-world Environments
*Important Dates* Manuscript Summary Submission Deadline: 30 September 2025 Manuscript Submission Deadline: 19 December 2025
*Submission* We are accepting the following article types: - Brief Research Report - Data Report - Editorial - General Commentary - Hypothesis and Theory - Methods - Mini Review - Opinion - Original Research - Perspective - Policy and Practice Reviews - Review - Systematic Review - Technology and Code
*Topic Editors* - Sahisnu Mazumder, Javier Felip Leon and Ramesh Manuvinakurike (Intel Labs, USA) - Maike Paetzel-Prüsmann (Johannes Gutenberg University Mainz, Germany) - Peng Hu and Hao Wang (Sichuan University, China) - Xin Yang (Southwestern University of Finance and Economics, China) - Hanbo Zhang (National University of Singapore, Singapore)
If you have any questions, do not hesitate to get in touch!