• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Google teaches robots how to recognize objects by interacting with their environment

December 12, 2018   Big Data

Google’s teaching AI systems to think more like children — at least, when it comes to object recognition and perception. In a paper (“Grasp2Vec: Learning Object Representations from Self-Supervised Grasping“) and accompanying blog post, Eric Jang, a software engineer at Google’s robotics division, and Coline Devin, a Ph.D. student at Berkeley and former research intern, describe an algorithm — Grasp2Vec — that “learns” the characteristics of objects by observing and manipulating them.

Their work comes a few months after San Francisco-based startup OpenAI demonstrated a computer vision system — dubbed Dense Object Nets, or DON for short — that allows robots to inspect, visually understand, and manipulate object they’ve never seen before. And it’s based on cognitive developmental research on self-supervision, the Google researchers explained.

People derive knowledge about the world by interacting with their environment, time-tested studies on object permanence have shown, and over time learn from the outcomes of the actions they take. Even grasping an object provides a lot of information about it — for example, the fact that it had to be within reach in the moments leading up to the grasp.

“In robotics, this type of … learning is actively researched because it enables robotic systems to learn without the need for large amounts of training data or manual supervision,” Jang and Devin wrote. “By using this form of self-supervision, [machines like] robots can learn to recognize … object[s] by … visual change[s] in the scene.”

The team collaborated with X Robotics to “teach” a robotic arm that could grasp objects “unintentionally,” and in the course of training learn representations of various objects. Those representations eventually led to “intentional grasping” of tools and toys chosen by the researchers.

 Google teaches robots how to recognize objects by interacting with their environment

The team leveraged reinforcement learning — an AI training technique that uses a system of rewards to drive agents toward specific goals — to encourage the arm to grasp objects, inspect them with its camera, and answer basic object recognition questions (“Do these objects match?”). And they implemented a perception system that could extract meaningful information about the items by analyzing a series of three images: an image before grasping, an image after grasping, and an isolated view of the grasped object.

In tests, Grasp2Vec and the researchers’ novel policies achieved a success rate of 80 percent, and worked even in cases where multiple objects matched the target and where the target consisted of multiple objects.

“We show how robotic grasping skills can generate the data used for learning object-centric representations,” they wrote. “We then can use representation learning to ‘bootstrap’ more complex skills like instance grasping, all while retaining the self-supervised learning properties of our autonomous grasping system. Going forward, we are excited not only for what machine learning can bring to robotics by way of better perception and control, but also what robotics can bring to machine learning in new paradigms of self-supervision.”

Let’s block ads! (Why?)

Big Data – VentureBeat

Environment, Google, Interacting, Objects, recognize, Robots, Teaches, their
  • Recent Posts

    • Experimenting to Win with Data
    • twice-impeached POTUS* boasts: “I may even decide to beat [Democrats] for a third time”
    • Understanding Key Facets of Your Master Data; Facet #2: Relationships
    • Quality Match raises $6 million to build better AI datasets
    • Teradata Joins Open Manufacturing Platform
  • Categories

  • Archives

    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited