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Cloud Robotics

2013.10.6

Sayed Chhattan Shah, PhD

Electronics and Telecommunications Research Institute, Koreahttps://sites.google.com/site/chhattanshah/

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Acknowledgements

Ken Goldberg, UC Berkeley, “Cloud Robotics”

Guoqiang Hu, NTU Singapore, “Cloud Robot-ics: Architecture, Challenges and Applica-tions”

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Outline

Introduction

Applications

Challenges

Current Research

Future Research Directions

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Cloud Robotics

Robots that rely on cloud-computing infrastructure to access vast amounts of processing power and data

Allow robots to offload compute-intensive tasks Image processing

Voice recognition

Robots can download new skills instantly

Enabling Factors

Mobile Devices

Wireless networks

Rapidly expanding Internet resources

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Benefits

Provides a shared knowledge database

Organizes and unifies information about the world in a format usable by robots

Offloads heavy computing tasks to the cloud Cheaper, lighter, easier-to-maintain hardware Longer battery life  Less need for software pushes/updates CPU hardware upgrades are invisible & hassle-free

Skill / Behavior Database Reusable library of “skills” or behaviors that map to per-

ceived task requirements / complex situations

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Example

Cloud-enabled Object Recognition

Google Goggles project

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Example

Robot Goggles

Upload images -> Download Semantic• Object name • 3D model, mass, materials, friction properties• Usage instructions - function, how to grasp, operate• Context and Domain knowledge

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Example

Matrix Movie Scene

For humans, still science fiction

For robots?

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Example

Maps and Localization

Shared and highly detailed maps of the world stored in the cloud

Updates can be published and immediately used

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Cloud Robotics and Networked Robots

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Cloud Robotics and Networked Robots

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Cloud Robotics and Networked Robots

Peer-based Model

Proxy-based Model

Clone-based Model

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Cloud Robotics Projects

Researchers at ASORO laboratory have built a cloud computing infrastructure to gener-ate 3-D models of environments

Allowing robots to perform simultaneous localization and mapping much faster than by relying on their onboard computers

• SLAM refers to a technique for a robot to build a map of the environ-ment without a priori knowledge, and to simultaneously localize itself in the unknown environment

The backend system consists of a Hadoop distributed file system that can store data from laser scanners, odometer data, or images and video streams from cameras

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Cloud Robotics Projects

At LAAS, Jean-Paul Laumond, and colleagues are creating object databases for robots to simplify the planning of ma-nipulation tasks like opening a door

The idea is to develop a software framework where objects come with a "user manual" for the robot to manipulate them

This manual would specify, for example, the position from which the robot should manipulate the object 

The approach tries to break down the computational com-plexity of manipulation tasks into simpler, decoupled parts: 

A simplified manipulation problem based on the ob-ject's "user manual," and

A whole-body motion generation by an inverse kine-matics solver, which the robot's computer can solve in real time

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Cloud Robotics Projects

 Gostai, a French robotics firm, has built a cloud robotics in-frastructure called GostaiNet, which allows a robot to per-form speech recognition, face detection, and other tasks remotely

Gostai's Jazz telepresence robot uses the cloud for video recording and voice synthesis

Cloud Robotics

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Challenges

Limited Resources

Mobility

Limited Power

Dynamic Network Environment

Security

Computation Challenges

Offload decision

Offload strategy

Communication Challenges

Data transfer time

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Cloud Robotics

Same as:

Remote computing?

Mobile cloud computing?

Mobile Grid Computing?