Original research papers
Submissions of original research on compositional and modular learning for robotics.
Machine Learning/CORE Unranked
CompRobotics2026 2026
Compositional and Modular Learning in the Era of Scaling in Robotics
The IROS 2026 Workshop on Compositional and Modular Learning in the Era of Scaling in Robotics explores how compositional and modular approaches can enhance learning in robotics by leveraging structural assumptions to reduce sample complexity. It focuses on integrating scaled foundation models with data-scarce modalities like force and tactile sensing, and addresses challenges in modular abstractions, embodiment transfer, and dextrous manipulation.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Work-in-progress papers
Papers presenting ongoing research are welcome.
Position papers
Papers offering conceptual or speculative viewpoints are encouraged.
Research areas in scope
01
Focus Areas
Modality CompositionInterfacing ModulesRole of Agents in Robotics
02
Topics of Interest
Compositional and modular learning for roboticsFoundation models and compositionality in roboticsMulti-modal learning with force and tactile sensingModular systems composing scaled and data-scarce modality representationsAbstractions for effective modularization in roboticsLong-horizon manipulation with composable skillsEmbodiment transfer of policiesModular architectures for dextrous manipulationImitation of compositional human actionsData-efficient learning and sample complexity in modular systemsModular agents for real-world manipulation
Policies worth checking twice
- Submissions must be in IEEE IROS format (two-column), up to 8 pages including acknowledgments and references.
- Workshop papers are non-archival.
- Concurrent submissions to other venues are allowed.
- Review process is double-blind.
- Submissions must be made via OpenReview.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026