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:

• Learning representations for robotic perception and control

• Learning robot foundation models or general-purpose knowledge systems for robotics

• Imitation learning for robotics, e.g. by behavioral cloning and/or inverse reinforcement learning

• Reinforcement learning for control of physical robots

• Model-based and model-free learning for robotic control and decision-making

• Combination of learning- and planning-based approaches in robotics

• Probabilistic learning and representation of uncertainty in robotics

• Automatic robotic data generation for learning methods in robotic

• Learning for Robot Task and Motion Planning

• Learning for multimodal robot perception, sensor fusion, and robot vision

• Learning for human-robot interaction and robot instruction by natural language, gestures as well as alternative devices

• Learning for hardware design and optimization

• Learning approaches to robot safety and safety approaches applicable to learning based robotic systems.

• Applications of robot learning in robot manipulation, navigation, locomotion, driving, flight, and other areas of robotics

• Robot systems, hardware, and sensors for learning and data-driven approaches

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