Boston, MI, U.S.A. | July, 2017

Robotics: Science and Systems (RSS 2017) Workshop

New Frontiers for Deep Learning in Robotics

Scope  Programme 


In this workshop a wide range of renowned experts will discuss deep learning techniques at the frontier of research that are not yet widely adopted, discussed, or well-known in our community. We carefully selected research topics such as Bayesian deep learning, generative models, or deep reinforcement learning for planning and navigation that are of high relevance and potentially groundbreaking for robotic perception, learning, and control. The workshop introduces these techniques to the robotics audience, but also exposes participants from the machine learning community to real-world problems encountered by robotics researchers that apply deep learning in their research.

This workshop is the successor of the very successful "Deep Learning in Robotics" workshop at last year’s RSS. Our goal is to bring researchers from the machine learning and robotics communities together to discuss and contrast the limits and potentials of new deep learning techniques, as well as propose directions for future joint research between our communities.

Call for Contributions

The workshop is complemented by contributed research papers that will be presented with 3 minute lightning talks and in an interactive poster session. We explicitly encourage the submission of papers describing work in progress, or containing preliminary results the authors with to discuss with the community. We invite contributions spanning the areas of deep learning, computer vision and robotics. We explicitly encourage the submission of papers describing work in progress, or containing preliminary results to discuss with the community. Submissions should follow the usual RSS guidelines for style and length (up to 6 pages). The accepted papers will be published on the workshop website.
In addition we encourage the community to submit questions for the speakers and panel before the workshop via web form. We hope to stimulate an interactive discussion this way. Question submission URL:


The topics of interest comprise, but are not limited to:
  • scene understanding
  • semi-supervised learning, low-shot learning
  • weakly supervised learning in the presence of noisy and unreliable labels
  • Bayesian deep learning and the importance of uncertainty and reliable confidence measures
  • deep networks as a sensor, sensor fusion with deep networks
  • active learning, incremental learning
  • generative models and their potentials for scene understanding and semi-supervised learning
  • novel weakly supervised or unsupervised training regimes
  • domain adaptation and transfer learning
  • generative models for reinforcement learning
  • inverse reinforcement learning, learning from visual demonstration
  • reinforcement learning for hierarchical tasks, complex tasks, non-Markovian tasks
  • case studies: when does state-of-the-art deep learning fail in robotics?
  • success stories: where did deep learning enable breakthroughs in robotics?
  • utilizing robotic technology to create novel datasets comprising interaction, active vision etc.
  • deep learning for embedded systems or platforms with limited computational power

Paper Submission Deadline

May 15, 2017
May 28, 2016 (extended) (anywhere on the planet)
Submit via eMail to  

Submissions should follow the usual RSS guidelines for style and length (up to 6 pages, double blind is optional).

RSS Workshop Date

July 15, 2017


Deep Learning Workshop at Robotics: Science and Systems Conference

MIT, Boston, USA

July 15, 2017

The following have agreed to join the list of speakers for our workshop (and will take part in the panel discussion):
Bayesian Deep Learning: Yarin Gal (University of Cambridge)
Learning to Navigate: Piotr Mirowski (DeepMind)
Generative Models for Reinforcement Learning: Pieter Abbeel (UC Berkeley/OpenAI)
Challenges of Embodied Deep Learning: Yann LeCun (Facebook, NYU)
Learning and Cognitive Robotics: Josh Tenenbaum (MIT) (tentative)
A Neuroscience Perspective on Deep Learning: David Cox (Harvard)
Generative Models: Aaron Courville (Université de Montréal)


Australian Centre for Robotic Vision