Applying machine learning to real-world systems such as robotics 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 and logistics, transportation and monitoring, 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.

Lifelong learning, transfer, and continuous improvement during deployment is the most likely path to break that barrier. 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.

Scope of contributions:

Important dates

Invited Speakers



In Pacific Time (San Francisco Time)

07:00 - 07:15 Opening Remarks
07:15 - 07.30 Contributed Talk 1
07:30 - 08:15 Panel: Learning from and Interacting with Humans (Q&A 1)
08:15 - 08:45 Break
08:45 - 09:45 Poster Session 1
09:45 - 10:30 Panel: Domains and Applications (Q&A 2)
10:30 - 15:15 Break
15:15 - 16:15 Panel: Self- and Unsupervised Learning (Debate)
16:15 - 16:30 Contributed Talk 2
15:30 - 17:00 Break
17:00 - 18:00 Poster Session 2
18:00 - 18:45 Panel: End2End or Modular Systems (Q&A 3)
18:45 - 19:00 Closing Remarks

Program Committee


Submission Instructions

Submissions should use the NeurIPS Workshop template available here and be 4 pages (plus as many pages as necessary for references). The reviewing proces will be double blind, so please submit as anonymous by using ‘\usepackage{neurips_wrl2021}’ in your main tex file.

Accepted papers and eventual supplementary material will be made available on the workshop website. However, this does not constitute an archival publication and no formal workshop proceedings will be made available, meaning contributors are free to publish their work in archival journals or conference.

Submissions can be made at https://cmt3.research.microsoft.com/NEURIPSWRL2021/.


  1. Can supplementary material be added beyond the 4-page limit and are there any restrictions on it?

    Yes, you may include additional supplementary material, but we ask that it be limited to a reasonable amount (max 10 pages in addition to the main submission) and that it follow the same NeurIPS format as the paper. References do not count towards the limit of 4 pages.

  2. Can a submission to this workshop be submitted to another NeurIPS workshop in parallel?

    We discourage this, as it leads to more work for reviewers across multiple workshops. Our suggestion is to pick one workshop to submit to.

  3. Can a paper be submitted to the workshop that has already appeared at a previous conference with published proceedings?

    We will not be accepting such submissions unless they have been adapted to contain significantly new results (where novelty is one of the qualities reviewers will be asked to evaluate). However, we will accept submissions that are under review at the time of submission to our workshop. For instance, papers that have been submitted to the International Conference on Robotics and Automation (ICRA) 2021 can be submitted to our workshop.

  4. My real-robot experiments are affected by Covid-19. Can I include simulation results instead?

    If your paper requires conducting experiments on physical robots and access to the experimental platform is limited due to Covid-19 workplace access restrictions, whenever possible, you may validate your methods through simulation.


For any faher questions, you can contact us at neuripswrl2021@robot-learning.ml