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Multimodal perception-driven decision-making for human-robot interaction: a survey.

Wenzheng Zhao1, Kruthika Gangaraju1, Fengpei Yuan1

  • 1Department of Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA, United States.

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Summary

Robots use multimodal perception, integrating vision, language, and touch, to make better decisions in complex environments. This review covers advancements and challenges in multimodal perception-driven decision-making (MPDDM) for human-robot interaction (HRI).

Keywords:
human-robot interactionmultimodal fusionmultimodal perceptionrobot decision-makingrobust autonomy

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Multimodal perception is key for robots to understand complex environments and human users by integrating diverse sensory data.
  • This capability is crucial for robot decision-making in dynamic and complex settings.

Purpose of the Study:

  • To provide a comprehensive review of multimodal perception and its integration with decision-making in robotics (2004-2024).
  • To systematically summarize multimodal perception-driven decision-making (MPDDM) frameworks and their application in human-robot interaction (HRI).

Main Methods:

  • Systematic literature review of MPDDM frameworks from 2004-2024.
  • Analysis of methodologies used in human-robot interaction (HRI).
  • Identification and analysis of key challenges in multimodal perception and decision-making.

Main Results:

  • Summarized existing MPDDM frameworks, highlighting advantages in dynamic environments.
  • Identified key challenges including technical integration, sensor noise, adaptation, domain generalization, and safety/robustness.
  • Reviewed methodologies employed in human-robot interaction (HRI).

Conclusions:

  • Future research should focus on adaptive multimodal fusion, efficient learning paradigms, and human-trusted decision-making frameworks.
  • Advancements in MPDDM are crucial for improving HRI.
  • Addressing challenges in sensor noise, adaptation, and robustness is vital for reliable robotic systems.