Uploaded January 2025 | Updated September 2026, 12 hours ago
In this presentation, researcher Dr Fethiye Irmak Dogan discusses her work on how robots can use explainability to enhance human-robot interaction, ensuring their ethical and transparent integration into human lives. She highlights two specific use cases: using robot explanations to resolve ambiguities in user instructions and incorporating human explanations into a robot's decision-making process to generate socially appropriate behaviours. "Ambiguities are inevitable during human-robot interaction," she notes, demonstrating how explainability can help identify sources of uncertainties and address them effectively. She also details their recent efforts to develop robot behaviours that align with social norms and human preferences across various environments. She showcases their system that integrates Large Language Models (LLMs) for common-sense reasoning with human explanations through a generative deep neural network architecture to predict socially appropriate actions.
In this presentation, researcher Dr Fethiye Irmak Dogan discusses her work on how robots can use explainability to enhance human-robot interaction, ensuring their ethical and transparent integration into human lives. She highlights two specific use cases: using robot explanations to resolve ambiguities in user instructions and incorporating human explanations into a robot's decision-making process to generate socially appropriate behaviours. "Ambiguities are inevitable during human-robot interaction," she notes, demonstrating how explainability can help identify sources of uncertainties and address them effectively. She also details their recent efforts to develop robot behaviours that align with social norms and human preferences across various environments. She showcases their system that integrates Large Language Models (LLMs) for common-sense reasoning with human explanations through a generative deep neural network architecture to predict socially appropriate actions.
