Applying machine learning to real-world systems such as robots has been an important part of the NeurIPS community in past years. Progress in machine learning has enabled robots to demonstrate strong performance in helping humans in some household and care-taking tasks, manufacturing, logistics, transportation, and many other unstructured and human-centric environments. While these results are promising, access to high-quality, task-relevant data remains one of the largest bottlenecks for successful deployment of such technologies in the real world.

Methods to generate, re-use, and integrate more sources of valuable data, such as lifelong learning, transfer, and continuous improvement could unlock the next steps of performance. However, accessing these data sources comes with fundamental challenges, which include safety, stability, and the daunting issue of providing supervision for learning while the robot is in operation. Today, unique new opportunities are presenting themselves in this quest for robust, continuous learning: large-scale, self-supervised and multimodal approaches to learning are showing and often exceeding state-of-the-art supervised learning approaches; reinforcement and imitation learning are becoming more stable and data-efficient in real-world settings; new approaches combining strong, principled safety and stability guarantees with the expressive power of machine learning are emerging.

This workshop aims to discuss how these emerging trends in machine learning of self-supervision and lifelong learning can be best utilized in real-world robotic systems. We bring together experts with diverse perspectives on this topic to highlight the ways current successes in the field are changing the conversation around lifelong learning, and how this will affect the future of robotics, machine learning, and our ability to deploy intelligent, self-improving agents to enhance people's lives.

Our speaker talks have been prerecorded and are available on YouTube. The talks will NOT be replayed during the workshop. We encourage all participants to watch them ahead of time to make the panel discussions with the speakers more engaging and insightful.

More information can be found on the website: http://www.robot-learning.ml/2021/.