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A simulated dataset for proactive robot task inference from streaming natural language dialogues.

Haifeng Xu1, Chunwen Li1, Xiaohu Yuan2

  • 1Department of Automation, Tsinghua University, Beijing, China.

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This new dataset helps proactive robots understand implicit human needs from conversations. It supports research in natural language understanding and autonomous task inference for improved human-robot interaction.

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Area of Science:

  • Robotics and Human-Computer Interaction
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Existing human-robot interaction datasets often focus on explicit commands.
  • Proactive robots require understanding implicit human needs from natural language.
  • Realistic workplace scenarios are crucial for developing effective human-robot collaboration.

Purpose of the Study:

  • To introduce a novel dataset for training proactive robots.
  • To capture implicit task requests within multi-party dialogues in simulated workplaces.
  • To facilitate research in natural language understanding and intent recognition for robots.

Main Methods:

  • Generation of 10,000 synthetic dialogues using a large language model pipeline.
  • Inclusion of 10 diverse workplace scenarios (e.g., biotech, legal, game development).
  • Focus on common workplace tasks like item borrowing, distribution, and processing.

Main Results:

  • A comprehensive dataset of natural language conversations reflecting implicit requests.
  • Coverage of both task-related and casual conversations in realistic settings.
  • A valuable resource for advancing the capabilities of proactive robotic systems.

Conclusions:

  • The dataset enables significant advancements in proactive robot capabilities.
  • It supports research in autonomous task inference and nuanced natural language understanding.
  • This resource is key for developing more intuitive and helpful human-robot interactions.