Robotics & Automation News

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Endurance Robots launches open source chatbot platform

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formitchev selfiebot chatbot
George Formitchev, CEO of Endurance Robots, demonstrates the company’s new chatbot platform

Endurance Robots has developed an open source platform to enable developers to build their own chatbots. 

CEO George Formitchev says the system is capable of facilitating the development of something like Jibo, Alexa, or Siri – some of the more well known chatbots on the market.

He adds that the SelfieBot Endurance ChatBot allows you to create your own personal assistant to “answer your emails and to communicate with you and on your behalf”.

Formitchev says the applications of the system could be “to help you learn a foreign language and to be a key addition in your everyday life”.

Endurance has a wide range of robotic solutions, including lasers for industrial applications, and like its chatbot most of it is open source and instructions on how to get started are on the company’s website.

All’s well that ROS well: Robot Operating System taking over the world

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PR2 robot, willow garage
The PR2 robot, developed by Willow Garage, practises its alien greeting

lockIf aliens from beyond our solar system or even our galaxy were to land on Earth and wanted to communicate with intelligent machines that can move around and shake hands, chances are that one of the first in line to greet our otherworldly visitors will be a robot with a brain built using the Robot Operating System. 

The simple reason for this is that ROS is one of the most ubiquitous platforms for robotics in the world today, helped largely by the fact that it is an open-source project, in contrast to the majority of robot operating systems which are proprietary and closed.

Described as a collection of frameworks for robot software development, ROS was originally developed in 2007 at Stanford University’s Artificial Intelligence Laboratory. ROS was then extended by Willow Garage, and that’s when it really started taking off.  [Read more…] about All’s well that ROS well: Robot Operating System taking over the world

Open Source Robotics Foundation teams up with Toyota Research Institute for software research

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toyota-car-tech

Tools for robotics and autonomous vehicles central to the two organisations

The Open Source Robotics Foundation and the Toyota Research Institute are to work together on developing new robotics and automation technologies. 

In addition, TRI has made a charitable contribution of $1 million to OSRF to support the organization’s goals to develop and promote the growth of the open source robotics community and to support the development of the Robot Operating System (ROS) and Gazebo.

 

Gill Pratt, TRI CEO, says: “I’ve witnessed first-hand the value of the Open Source Robotics Foundation. Of the 23 teams that competed in the Darpa Robotics Challenge 18 used ROS and 14 used Gazebo.

“Through this charitable contribution, TRI will support efforts to grow the capabilities of ROS and Gazebo, not only for TRI, but also for the hundreds of thousands of members of the open source robotics community.”  [Read more…] about Open Source Robotics Foundation teams up with Toyota Research Institute for software research

Google open sources TensorFlow Serving

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Tensor_Flow_Diagram1_Serving_FINALAfter releasing its deep learning or machine learning library TensorFlow as open source software a few months ago, Google has now followed up by open sourcing TensorFlow Serving, which the company says makes it easier to take models into production.

Noah Fiedel, Google software engineer, writes on his blog: “TensorFlow Serving makes the process of taking a model into production easier and faster.

“It allows you to safely deploy new models and run experiments while keeping the same server architecture and APIs.”

TensorFlow Serving is written in C++ and supports Linux. Fidel says the software requires minimal overhead. “In our benchmarks we recoded approximately 100,000 queries per second per core on a 16 vCPU Intel Xeon E5 2.6 GHz machine, excluding gRPC and the TensorFlow inference processing time.”