Workshop on Continual Robot Learning from Humans

About
Humans excel at adapting to ever-changing environments, learning from one another, and continuously refining their knowledge and skills over time. The ability to learn continually from human interactions—either through direct demonstrations, communications, proactive feedback, collaboration, or shared norms and values—has been fundamental to human progress. As robots become more integrated into our daily lives, incorporating this adaptive learning in robotics has the potential to revolutionize how they function in dynamic environments, and more importantly, in human society.

How can we develop robots that continually learn and evolve through human interaction? To address this question, the Workshop on Continual Robot Learning from Humans aims at bridging the gap between human adaptive learning and robotic capabilities. The workshop will explore a range of topics, including human-in-the-loop learning, long-term human-robot partnerships, learning societal norms and values, and fostering robot social intelligence. We invite contributions that introduce new theoretical frameworks, present experimental findings, or showcase applications that advance the field of continual learning from humans. Our goal is to tackle emerging challenges and present innovative frameworks that enable robots to learn, adapt, and thrive through ongoing human engagement—fostering an interdisciplinary dialogue that pushes the boundaries of robot learning.

Schedule

Time (PST)
8:50 am - 9:00 am	Organizers Introductory Remarks
9:00 am - 9:30 am	Yonatan Bisk Keynote Talk Abstract To be determined.
9:30 am - 10:00 am	Mahi Shafiullah Keynote Talk Abstract To be determined.
10:00 am - 10:30 am	Break and Poster Session
10:30 am - 11:00 am	Homanga Bharadhwaj Observational Learning through Visual Imitation of Humans Abstract To be determined.
11:00 am - 11:30 am	Yilun Du Keynote Talk Abstract To be determined.
11:30 am - 12:00 pm	Chelsea Finn Keynote Talk Abstract To be determined.
12:00 pm - 12:30 pm	Maja Mataric Keynote Talk Abstract To be determined.

Call for Papers
We invite original contributions presenting novel ideas and research on continual robot learning from humans. We particularly encourage work emphasizing interdisciplinary approaches and practical applications.

Important Dates

Event	Date
Submission Deadline	May 19th, 2025 (23:59 AoE)
Notification	June 5th, 2025
Camera Ready	June 12nd, 2025
Workshop	June 21st (9:00am - 12:30pm), 2025

Submission Guidelines

Submissions should follow 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, excluding references, will be reviewed for inclusion in the workshop program. All papers must be submitted as anonymized PDFs for double-blind review via OpenReview.

Accepted papers will be presented during the workshop (either in-person or remotely) and featured in a poster session. The proceedings will be treated as non-archival, allowing future conference or journal submissions.

Paper Topics

Relevant topics include, but are not limited to:

Human-in-the-loop methods and feedback-driven robot learning
Long-term human-robot collaboration and partnerships
Adapting to evolving human norms, values, and social context
Integrated Human Feedback to Boost Robot Safety and Performance
Proactive Feedback Strategies for Efficient Robot Learning
Dynamic Models of Human Behavior in Evolving Interactions
Frameworks for Continuous Social Learning and Robust Memory Retention
Adaptive Assistance Systems for Long-Term Human Support
Benchmarks for Evaluating Enduring Human-Robot Collaborations
Balancing Multiple and Conflicting Human Preferences
Mechanisms for Robots to Interpret and Adapt to Changing Social Norms
Pluralistic Human Values for Improved Robot Social Acceptability
Theory of Mind Applications in Robots for Behavior Interpretation and Prediction
Social Reasoning Approaches in Complex Environments
Pedagogical Methods for Enhancing Knowledge Transfer in Robots