To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions

Daniel Tanneberg, Felix Ocker, Stephan Hasler, Joerg Deigmoeller, Anna Belardinelli +4 more
3/19/2024
cs.ROcs.AI

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Abstract

How can a robot provide unobtrusive physical support within a group of humans? We present Attentive Support, a novel interaction concept for robots to support a group of humans. It combines scene perception, dialogue acquisition, situation understanding, and behavior generation with the common-sense reasoning capabilities of Large Language Models (LLMs). In addition to following user instructions, Attentive Support is capable of deciding when and how to support the humans, and when to remain silent to not disturb the group. With a diverse set of scenarios, we show and evaluate the robot's attentive behavior, which supports and helps the humans when required, while not disturbing if no help is needed.

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Cite this paper

@article{tanneberg2024help,
  title  = {To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions},
  author = {Daniel Tanneberg and Felix Ocker and Stephan Hasler and Joerg Deigmoeller and Anna Belardinelli and Chao Wang and Heiko Wersing and Bernhard Sendhoff and Michael Gienger},
  year   = {2024},
  eprint = {2403.12533},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.12533v3}
}

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