ReviewFrontiers in robotics and AI2025
Multimodal perception-driven decision-making for human-robot interaction: a survey.
Review in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation.Sensors (Basel, Switzerland) · 2026Article
- BioLAMR: A Biomimetically Inspired Large Language Model Adaptation Framework for Automatic Modulation Recognition.Biomimetics (Basel, Switzerland) · 2026Article
- Semantic-Physical Sensor Fusion for Safe Physical Human-Robot Interaction in Dual-Arm Rehabilitation.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Multimodal perception is essential for enabling robots to understand and interact with complex environments and human users by integrating diverse sensory data, such as vision, language, and tactile information. This capability plays a crucial role in decision-making in dynamic, complex environments. This survey provides a comprehensive review of advancements in multimodal perception and its integration with decision-making in robotics from year 2004-2024. We systematically summarize existing multimodal perception-driven decision-making (MPDDM) frameworks, highlighting their advantages in dynamic environments and the methodologies employed in human-robot interaction (HRI). Beyond reviewing these frameworks, we analyze key challenges in multimodal perception and decision-making, focusing on technical integration and sensor noise, adaptation, domain generalization, and safety and robustness. Finally, we outline future research directions, emphasizing the need for adaptive multimodal fusion techniques, more efficient learning paradigms, and human-trusted decision-making frameworks to advance the HRI field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.