Call for Papers

The Conference on Robot Learning (CoRL) is an annual international conference aiming to bring together the robotics and machine learning research communities. It focuses on the increasingly important role of learning in robotics and its interaction with other areas of robotics. The aim of CoRL 2024 is to publish significant original research at the intersection of robotics and machine learning. CoRL is a selective, single-track international conference addressing theory and practice of machine learning for robots. CoRL welcomes papers in areas such as:

Submissions should focus on a core robotics problem and demonstrate the relevance of proposed models, algorithms, datasets, and benchmarks to robotics. Authors are encouraged to report real-robot experiments or provide convincing evidence that simulation experiments are transferable to real robots. Submissions without a robotics focus will be returned without review.

All Submissions should include a Limitations section, explicitly describing limiting assumptions, failure modes, and other limitations of the results and experiments and how these might be addressed in the future.

Authors are also encouraged to submit video, code and data as supplementary materials.

For paper submission format and timeline, see: Instructions for Authors