In an era where adaptability and continual learning are paramount for autonomous systems, the field of robotics stands at a critical juncture. The complexity and unpredictability of the real world present a formidable challenge: a robot trained in a controlled environment, even with sophisticated simulation or rich offline datasets, may falter when faced with the myriad, dynamic conditions outside the lab. The transition from static, predictable settings to the nuanced variability of the physical world necessitates robots that are not merely reactive but are endowed with the capacity for proactive adaptation and self-improvement.

This special issue, titled "Robot Learning to Adapt and Improve in the Real World," aims to cast a spotlight on the latest research that empowers robots with such capabilities. We seek to explore cutting-edge methodologies that facilitate continuous adaptation to new tasks and environments, enhance generalization to previously unseen scenarios, and refine the proficiency of existing robotic skills.

We welcome submissions that delve into the theoretical underpinnings, algorithmic advancements, and empirical evaluations of robot learning systems.

We invite submissions of original research articles, reviews, and potentially high-impact short communications that address these topics. Contributions should not only present novel approaches but also substantiate their applicability in real-world scenarios through rigorous experimentation or substantial empirical evidence.