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Generative AI for Intelligent Interactive Robots

Generative AI for Intelligent Interactive Robots

This workshop explores generative AI's potential to enhance human-robot interactions, focusing on NLP, adaptive learning, multimodal AI, and ethical considerations. Date: 05.11.2024 | 10.45 - 12.15

PAL Robotics, organised at the #ADRF24, the workshop titled, ''Generative AI for Intelligent Interactive Robots'' and our partner LAAS-CNRS also participated.

This workshop explored the transformative potential of generative AI in robotics, focusing on how these technologies can enhance robots' interactions across sectors like healthcare and customer service. As robots take on more roles, generative AI offers promising methods to make robot-human interactions more natural and intelligent.

Key topics included applying natural language processing (NLP) for improved understanding and generation of human language, adaptive learning for tailoring interactions to individual user preferences, and multimodal AI that integrates visual, auditory, and textual data for richer interaction experiences.

Participants also discussed ethical concerns around using generative AI in interactive robots, particularly regarding privacy, consent, and bias. By uniting experts from AI, robotics, and human-computer interaction, this workshop aimed to encourage interdisciplinary innovation toward developing the next generation of interactive robots that are both effective and ethically grounded.

 Many thanks to Francesco Ferro and our project's coordinator Nicolas Mansard for their great presentation! 

Also, our synergy project MANOLO participated in this workshop, exploring more connections between the two projects and delving into AI systems.

Organizer: Francesco Ferro - PAL Robotics

Speakers:

  • Ricardo Simon Carbajo - CeADAR
  • Nicolas Mansard - CNRS
  • Aurélie Clodic - LAAS-CNRS
  • Christophe Leroux - CEA
  • Rudolph Triebel - DLR
  • Francesco Ferro - PAL Robotics

Learn more about ADRF24: ADRForum

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This work is supported by the AGIMUS project funded by the European Union under GA no.101070165. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.

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