John Kubiatowicz

About

John Kubiatowicz is a pioneering computer scientist whose research sits at the intersection of distributed systems, cloud computing, and robotics. He is best known for developing the concept of **Fog Robotics**, a distributed computing paradigm that addresses critical limitations of centralized cloud robotics—including latency, privacy, security, and bandwidth constraints—by intelligently distributing computation across edge and cloud resources. Kubiatowicz's most influential contribution is the FogROS ecosystem, a suite of frameworks built atop ROS 2 that enables robots to seamlessly offload demanding computational tasks—such as deep learning-based object recognition and grasp planning—to on-demand cloud infrastructure. His foundational 2019 paper on Fog Robotics has garnered over 80 citations, while the FogROS and FogROS2 platforms have collectively attracted tens of citations, reflecting strong community adoption. His work has progressively tackled real-world deployment challenges, yielding specialized extensions for secure global connectivity (FogROS2-SGC), latency-sensitive applications (FogROS2-LS), fault tolerance (FogROS2-FT), and automated server configuration (FogROS2-Config). Through this sustained research program, Kubiatowicz has meaningfully shaped how the robotics community thinks about scalable, reliable, and economically viable robot autonomy in industrial, automotive, and service contexts.

Research Focus

Key Achievements

Top Papers

1. 1
   A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

   81 citations · 2019
2. 2
   FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2

   38 citations · 2023
3. 3
   FogROS: An Adaptive Framework for Automating Fog Robotics Deployment

   37 citations · 2021
4. 4
   FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity

   19 citations · 2023
5. 5
   FogROS2-LS: A Location-Independent Fog Robotics Framework for Latency Sensitive ROS2 Applications

   11 citations · 2024
6. 6
   A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

   7 citations · 2019
7. 7
   FogROS G: Enabling Secure, Connected and Mobile Fog Robotics with Global Addressability

   5 citations · 2022
8. 8
   FogROS2-Config: A Toolkit for Choosing Server Configurations for Cloud Robotics

   4 citations · 2024
9. 9
   FogROS2-FT: Fault Tolerant Cloud Robotics

   2 citations · 2024
10. 10
    FogROS2-PLR: Probabilistic Latency-Reliability for Cloud Robotics

    1 citations · 2025

Key Collaborators

Contact & Links