Cloud and Fog Robotics in the Age of Deep Learning

Robotics: Science and Systems (RSS) 2019 Workshop

23rd June 2019, Messe Freiburg, Germany

Recent News

  • We have accepted 3 papers to the conference. See the papers page for details.


Cloud and fog robotics enables resource-constrained robots to utilize both on-robot and cloud resources for compute and storage. Recent progress in deep learning, cloud computing, and the advent of cloud robotics platforms from the likes of Google and Amazon make today an exciting time to consider cloud robotics.

However, cloud robotics comes with an often understated cost: offloading images, videos or high data-rate sensor measurements such as LIDAR can severely congest wireless networks, add to latency, and place a large burden on cloud compute resources. In this workshop, we aim to bridge advances in the computer systems and robotics communities to address how to best distribute networking, storage, and communication resources between robots and the cloud.

Accordingly, the objectives of this workshop are:

  1. To convene together researchers and industry experts from computer science, systems, robotics and deep learning to jointly discuss the challenges and define a roadmap for technology development.
  2. To identify promising applications of cloud robotics, such as offloading object detection, grasp planning, motion planning, mapping and localization for robots to enhance dexterous manipulation.
  3. To inform roboticists about algorithms for network-enabled services and introduce new cloud robotics platforms from Amazon, Google, and other university partners.

Call for Papers

We invite short papers of 2-6 pages with no space limit for references and supplementary materials. We also encourage "white papers'' or "deployment experience" papers from industry describing the challenges of adopting cloud robotics in practice. Submissions should follow the RSS paper format guidelines at:

Areas of interest include, but not limited to:

  • Cloud, Edge and Fog Robotics
  • Network enabled Robotics services for dexterous manipulation, mapping, visual perception, speech processing and other applications
  • Deep learning, model adaptation and inference serving over network
  • Collaborative deep learning of shared representations (e.g. sim to real transfer)
  • Security, Privacy and Control over data and models
  • Load balancing and resource allocation

Important Dates

Paper submissions due: June 7, 2019, Anywhere On Earth (UTC-12) (Extended Deadline)

Author Notifications: June 12, 2019

Camera-ready paper submission due: June 17, 2019

Workshop date: June 23, 2019

Time: 09:00 - 17:00

Location: Messe Freiburg, Germany. For further details, see the official RSS 2019 website.


We encourage participation from diverse communities, including, but not limited to, researchers in robotics, computer systems and industrial practitioners of cloud robotics. The workshop will be a unique opportunity to network with computer science, systems, robotics and deep learning community.

Invited Speakers

Our confirmed speakers are leaders in cloud robotics and computer systems from both academia and industry. Speakers include:

  • Ken Goldberg, UC Berkeley
  • Ron Alterovitz, University of North Carolina at Chapel Hill
  • Ludovic Righetti, New York University
  • Nicola Dragoni, Technical University of Denmark and Centre for Applied Autonomous Sensor Systems, Orebro University
  • James Kuffner, Director of the Toyota Research Institute
  • Gajamohan Mohanarajah, Rapyuta Robotics
  • Flavio Bonomi, Founder and CEO, Nebbiolo Technologies, Inc.
  • Ben Kehoe, iRobot Cloud Robotics
  • Thomas Moulard, AWS RoboMaker
  • Dr. Aakanksha Chowdhery, Google Brain
  • Dr. Dinesh Bharadia, UC San Diego
  • Titus Cieslewski, Univerity of Zurich and ETH Zurich

If you are interested in speaking or doing a demonstration of a technical product, please contact us below!


Sandeep Chinchali*

PhD Student

Stanford University

csandeep [at]

Ajay Tanwani*

Postdoctoral Scholar

UC Berkeley

ajay.tanwani [at]

Marco Pavone

Associate Professor

Stanford University

pavone [at]