Advances in learning-based methods for perception, decision making, and control continue to open up new possibilities for deployment on physical robot platforms. Recent examples are given by the considerable rate of progress in representation learning - enabling easier application for supervised and reinforcement learning to domains with image-based data. However, the development and evaluation of algorithmic progress are often constrained to simulation and rigid datasets, leading to overfitting to specific characteristics in these limited domains.

Experiments on physical platforms benefit from the complexity and variety of real-world data both for the generality of evaluation and richness of training data. While direct contact with the real-world provides a grounding for algorithmic performance, the question of deployment also introduces its own challenges for experimentation and reproducibility. Environments, tasks, and platforms have to be standardized, relevant, and broadly accessible. However, finding suitable compromises by improving the realism of datasets and simulators while addressing the limits of real-world experiments will be important to ensure that research insights survive the test of time.

The goal of the workshop is to discuss the challenges for machine learning research in the context of physical systems. This discussion involves the presentation of current methods and the experiences made during algorithm deployment on real-world platforms. 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.

Rather than merely focusing on applications of machine learning in robotics, as in the previous, successful iterations of the workshop, the new interdisciplinary panel will foster discussion on how real-world applications such as robotics can trigger various impactful directions for the development of machine learning and vice versa. To further this discussion, we aim to improve the interaction and communication across a diverse set of scientists who are at various stages of their careers. Instead of the common trade-offs between attracting a wider audience with well-known speakers and enabling early-stage researchers to voice their opinion, we encourage each of our senior presenters to share their presentations with a PhD student or postdoc from their lab. We also ask all our presenters - invited and contributed - 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.

Scope of contributions:

Important dates

Confirmed Speakers

Organizers

Schedule

TBD

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_wrl2020}’ 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/NEURIPSWRL2020/.

FAQ

  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).

  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.

Contacts

For any further questions, you can contact us at neuripswrl2020@robot-learning.ml