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


Schedule (in Pacific Time)

07:00 Introduction and opening remarks
07:15 Invited talk 1 - Raquel Urtasun
08:00 Contributed talk 1 - Best paper runner up
08:15 Break
08:45 Poster session 1
09:45 Invited talk 2 - Pete Florence
10:30 Invited talk 3 - Dorsa Sadigh
11:15 Break
15:00 Panel discussion
16:00 Invited talk 4 - Jemin Hwangbo
16:45 Contributed talk 2 - Best paper
17:00 Break
17:30 Invited talk 5 - Fabio Ramos
18:15 Poster session 2
19:15 Closing

Accepted Papers

Program Committee

We would like to thank the program committee for shaping the excellent technical program. In alphabetical order they are: Achin Jain, Adithyavairavan Murali, Akshara Rai, Alex Bewley, Ashvin Nair, Brian Ichter, Caterina Buizza, Coline Devin, Djalel Benbouzid, Dushyant Rao, Edward Johns, Jacob Varley, James Harrison, Jayesh Gupta, Jianwei Yang, Jie Tan, Johannes A. Stork, Jonathan Tompson, Karol Hausman, Kunal Menda, Marcin Andrychowicz, Marco Ewerton, Marko Bjelonic, Misha Denil, Nantas Nardelli, Nemanja Rakicevic, Octavio Antonio Villarreal Magaa, Panpan Cai, Peter Karkus, Raunak Bhattacharyya, Ruohan Wang, Sasha Salter, Siddharth Reddy, Spencer Richards, Takayuki Osa, Tomi Silander, Tuomas Haarnoja, Vikas Sindhwani, Walter Goodwin, Yevgen Chebotar, Yizhe Wu, Yunzhu Li

Manuscript 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

Poster and Camera-Ready Submission Instructions

Poster deadline (Nov 24, 2020 AOE)

Camera-ready paper deadline (Dec 4, 2020 AOE)


  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 (i.e. before Oct 9). For instance, papers that have been submitted to the conference on Robot Learning (CoRL) 2020 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


We are very thankful to our corporate sponsors, Naver Labs Europe and Google Brain, for enabling us to provide best paper awards and student registration fees.