Introduction

Modern robots, from assistive and social robots to self-driving cars, are increasingly operating in human-centered environments, requiring them to collaborate more closely with human users than ever before. Beyond performing useful tasks, these robots must also learn and adapt through human interactions to become more effective and intuitive partners. Designing robotic agents that can work alongside, learn from, and adapt to humans is a key research challenge, requiring solutions that address diverse interaction types—including demonstrations, corrections, natural language commands, and preference-based feedback. Another challenge lies in how robots interpret and respond to human input, which can be ambiguous, noisy, and vary across users. Robots must be able to adapt to different interaction styles and evolving user needs while maintaining robust and sample-efficient learning mechanisms. Unlike traditional machine learning settings, where large-scale datasets can be collected and labeled offline, human-in-the-loop learning often requires real-time adaptation with limited supervision, making efficient use of human guidance a critical challenge.

Addressing these challenges demands interdisciplinary collaboration across fields such as machine learning, cognitive science, and control theory. This workshop aims to bring together researchers from robotics, artificial intelligence, machine learning, and human-computer interaction to explore various approaches to human-in-the-loop robot learning. As robots are increasingly deployed in real-world environments, it is more crucial than ever for these agents to be able to correct mistakes on the fly and rapidly personalize their behavior to different users and scenarios. Through discussions on methodologies, benchmarks, and real-world applications, we aim to identify open problems and future directions in human-interactive robot learning.

Call for Papers

We are excited to announce the Call for Papers for the workshop. We invite original contributions presenting novel ideas, research, and applications relevant to the workshop’s theme.

Important Dates

Submission Guidelines

The submissions must use the RSS template, which is available either in LaTeX or Word format. The recommended paper length is 4 pages excluding references. However, any paper that is between 2 and 6 pages, again excluding references, will be reviewed for inclusion in the workshop program. There will be no archival proceedings and the authors of the accepted papers will be given a chance to opt in or out of having their papers on the workshop website. Authors will be asked to present their papers at the workshop in the form of posters. A subset of the accepted papers may be invited to deliver spotlight talks.

Submission Link: OpenReview Link

Paper topics

A non-exhaustive list of relevant topics:

• Interactive and online learning from human corrections
• Sample-efficient robot learning through human guidance
• Human-aware decision-making and policy adaptation in robotics
• Personalization and user-specific adaptation in robot learning
• Human trust and interpretability in interactive learning systems
• Human-in-the-loop learning in assistive and social robotics
• Multi-agent systems with human-robot interactions
• Learning from multimodal human feedback

Workshop Schedule (Tentative)

Invited Speakers

Andreea Bobu

MIT

Dražen Brščić

Kyoto University

Luis Figueredo

University of Nottingham

Matthew Gombolay

Georgia Tech

Shiwali Mohan

SRI International

Luka Peternel

TU Delft

Elliott Rouse

University of Michigan

Harold Soh

National University of Singapore

Siddhartha Srinivasa

University of Washington

Katia Sycara

CMU

Organizers

Yigit Korkmaz

USC

Ayano Hiranaka

USC

Anthony Liang

USC

Mike Hagenow

MIT

Takayuki Kanda

Kyoto University

Daniel Brown

University of Utah

Maegan Tucker

Georgia Tech

Erdem Bıyık

USC