Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 15, 2025

Cross-Modal Causal Inference Facilitates Home Intelligent Robots: Intent Understanding Bias Calibration and Interaction Failure Avoidance in a Dynamic Home Environment

View Full Paper
Ask AI
Bookmark
Share

Authors

YCYimin ChenTHTasriful Haque

Discussion

Loading...

Member takes

Overview

This analysis demonstrates improved intention judgment accuracy for service robots, suggesting effective interaction in dynamic settings.

Key Points

  • The proposed framework increases intent recognition accuracy by 24.3%, enhancing service robot effectiveness in dynamic environments.
  • With this approach, the recognition error rate drops by 62.1%, leading to fewer miscommunications between humans and robots.
  • Using a multimodal causal reasoning model enhances interaction success, even with sudden environmental changes.
  • Potential applications include smarter home automation, indicating that service robots could better serve households in varied conditions.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68af63efad7bf08b1eae4c5ehttps://doi.org/10.70702/bdb//fmua9805
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Modular Framework for Responsive and Explainable Robotic Assistance with Intention Prediction Using Human-Centric Digital Twins2026
  2. 2Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces2025 · 3 citations
  3. 3A Causally Inspired Counterfactual Evaluation Framework for Wearable Assistive Robots2026
  4. 4Intelligent Agents and Causal Inference: Enhancing Decision-Making through Causal Reasoning2024 · 5 citations
  5. 5Learning Multimodal Confidence for Intention Recognition in Human-Robot Interaction2024