About

Recent advances in machine learning have started a paradigm shift from task-specific models towards large general-purpose architectures. In the domains of language and vision we see large models such as GPT3, BERT, and CLIP that have opened avenues towards solving several applications and continue to cause an explosion of new ideas and possibilities. What does it take to bring the same level of advancements to the field of robotics - in order to build versatile agents that can be deployed in challenging environments? The goal of this workshop is to analyze how we can scale robotics towards the complexity of real world by leveraging pretrained models. We will discuss how to apply the concept of large scale pretraining to robotics, so as to enable models to learn how to process diverse, multimodal perception inputs, connect perception with action, and generalize across scenarios and form factors. In particular, we are interested in analyzing the domain of pretraining for robotics from several angles such as, and not limited to:

  • How do we build pre-trained reusable feature representations from complex inputs?
  • How do we learn world models that combine perception and actions?
  • What are the right kinds of priors that are helpful for optimization and task planning?
  • How do we leverage architectures and training methods that have been successful in other domains in robotics?
  • How do we efficiently fine-tune pretrained models for new downstream tasks?
  • How best to deal with the specificities of robotics such as expensive data collection and safety constraints?
We hope to connect researchers from the communities of deep learning, representation learning, classical robotics, and to induce collaborations in this exciting new domain, while providing a platform to discuss recent developments, challenges and tradeoffs.

Speakers and panelists

Call for papers

Important dates (all times AoE)

  • Submission Deadline: TBD
  • Accept/Reject Notification Date: TBD
  • Workshop: 29 May 2023

Call for papers

In this workshop, we aim to bring together machine learning and robotics researchers who work at the intersection of these fields

We invite researchers to submit work particularly in the following areas or areas related to them:

Posters

Accepted work will be presented as posters during the workshop. We encourage submissions of work in progress, as well as work that is not yet published.

Submission instructions

  • Submissions should be short papers up to 4 pages in PDF format.

Organizers

Contact

For questions and comments, please contact us.