In the proposed workshop, we aim to discuss the challenges and opportunities for machine learning research in the context of physical systems. This discussion involves the presentation of recent methods and the experiences made during the deployment on real-world platforms. Such deployment requires a significant degree of generalization. Namely, the real world is vastly more complex and diverse compared to fixed curated datasets and simulations. Deployed machine learning models must scale to this complexity, be able to adapt to novel situations, and recover from mistakes. Moreover, the workshop aims to strengthen further the ties between the robotics and machine learning communities by discussing how their respective recent directions result in new challenges, requirements, and opportunities for future research.

Following the success of previous robot learning workshops at NeurIPS, the goal of this workshop is to bring together a diverse set of scientists at various stages of their careers and foster interdisciplinary communication and discussion.
In contrast to the previous robot learning workshops which focused on applications in robotics for machine learning, this workshop extends the discussion on how real-world applications within the context of robotics can trigger various impactful directions for the development of machine learning. For a more engaging workshop, we encourage each of our senior presenters to share their presentations with a PhD student or postdoctoral researcher from their lab. Additionally, all our presenters - invited and contributed - are asked to add a ``dirty laundry’’ slide, describing the limitations and shortcomings of their work. We expect this will aid further discussion in poster and panel sessions in addition to helping junior researchers avoid similar roadblocks along their path.