Resource-Rational Robot Learning
The Rational Robots Workshop
CoRL 2025 | Seoul, South Korea | September 27, 2025
Recent advances in robot capabilities, fueled by data-hungry learning algorithms and large-scale foundational models, are undeniably exciting. However, as we marvel at these advancements, a critical question arises: is the current trajectory of "scaling data and compute is all you need" sustainable or even desirable for robotics? Myopically committing to this path risks creating systems that are uneconomical and ill-suited for the various constraints imposed by physical embodiments, thereby limiting widespread adoption. Instead, just as evolution nurtured the emergence of efficient brains or how mature engineering disciplines meticulously navigate resource-performance trade-offs, it is imperative that robot learning as a field embeds resource consideration at every stage of the robot learning deployment lifecycle -- from training and inference to continuous improvement and adaptation. This workshop will explore why designing for resource efficiency is beneficial, which resources should be considered for learning, and how they can be leveraged pragmatically to realize economical robot deployment.
Towards this end, we seek to advance robot learning towards resource rationality: careful deliberation on how the robot should judiciously use resources (such as computation, time, and human assistance) based on their cost-performance tradeoff through all phases of learning. Through this lens, the benefits of resource-rational learning can be explored across a diversity of perspectives, such as the importance of models for principled, sample-efficient learning and inference; effectiveness of simulated data to complement costly real-world samples; leveraging human instructions, feedback, and priors to ground what is important to learn; understanding how much sensing and information is needed for a task; and the implications of resource rationality for today's foundation models.
Resource rationality has broad impact for not only many different problems within robot learning, but more widely, to adjacent fields related to embodied intelligence, such as planning, cognitive science (understanding how human learning can be achieved given biological computational limitations), causality (understanding the causal relevance of data to decide how training resources should be expended), and systems design and engineering (how to build systems at the Pareto frontier of performance and resource utilization). Therefore, our intended audience includes the robot learning, planning, machine learning, cognitive science, causality, and robotic systems design communities, and we will select speakers and panelists who have expertise within these areas.
Our workshop aims to highlight the diversity of perspectives that compose resource-rational robot learning across the robot learning deployment lifecycle -- training, inference, and continuous improvement and adaptation -- through discussion-rich research questions. These research questions will be embedded throughout the workshop structure, from keynote and contributed talks to our closing debate and reflections on resource rationality.
Our workshop will feature a diversity of programming elements to explore resource rationality in robot learning. Specifically, our workshop features keynote talks, contributed talks, a poster session, all-day audience engagement featuring an in-workshop guided survey, a debate to critically analyze whether resource rationality should be a priority for the robot learning community, and culminating reflections on resource rationality.
We invite contributions that tackle resource rationality for robot learning from diverse perspectives, including but not limited to the following topics. We particularly encourage early-stage ideas, preliminary results, or in-progress work that can spark discussion and inspire new directions. Unfortunately, we are not able to accept submissions that have already been accepted to the main CoRL 2025 conference.
Papers should be submitted through OpenReview. Papers may be up to 8 pages, and should be formatted using the CORL 2025 LaTex template. Acknowledgments, References, and Appendix (optional) will not count towards the page limit, and submissions must be anonymized. Authors are encouraged to submit a supplementary file containing further details for reviewers, to be submitted through OpenReview as a single zip file.
The reviewing process will be double-blind, single-phase (i.e., no rebuttal).
The paper accepted to the workshop will be non-archival — there will be no formal proceedings. At least one author for each accepted paper must attend the workshop in-person.
Submission Deadline: Aug 15, 2025, AoE Aug 18, 2025, AoE
Author Notification: Sept 3, 2025, AoE
Camera Ready Deadline: Sept 22, 2025, AoE
Workshop Date: Sept 27, 2025
