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Article Dans Une Revue IEEE Robotics and Automation Magazine Année : 2022

Innovation Paths for Machine Learning in Robotics [Industry Activities]

Freek Stulp
Michael Spranger
  • Fonction : Auteur
Kim Listmann
  • Fonction : Auteur
Moritz Tenorth
  • Fonction : Collaborateur
George Konidaris
  • Fonction : Collaborateur
Pieter Abbeel
  • Fonction : Collaborateur

Résumé

Presents interviews conducted with robotics engineers discussing advances in artificial intelligence, with particular use in machine learning. Advances in artificial intelligence (AI), especially in machine learning (ML), are changing the business models of many companies, and creating entirely new ones. Recent research estimates that AI could boost profitability rates by 38% worldwide, leading to an economic boost of €12 trillion across a variety of industries by 2035. This immense number is an accumulation of many smaller numbers, related to the successful deployment of ML at individual companies, including smalland medium-sized enterprises (SMEs) and start-ups.
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Dates et versions

hal-03992530 , version 1 (16-02-2023)

Identifiants

Citer

Freek Stulp, Michael Spranger, Kim Listmann, Stephane Doncieux, Moritz Tenorth, et al.. Innovation Paths for Machine Learning in Robotics [Industry Activities]. IEEE Robotics and Automation Magazine, 2022, 29 (4), pp.141-144. ⟨10.1109/MRA.2022.3213205⟩. ⟨hal-03992530⟩
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