most citedTemporal Counterfactual Explanations of Behaviour Tree Decisions

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2026

Assessing Physical Frailty and Fall-Risk Indicators with Social Robots: An in situ Evaluation with Older Adults

Aniol Civit, Antonio Andriella, Alba Martínez +4

Frailty assessments are crucial to evaluate the risk of adverse events and the health and social care needs of older adults, yet their administration remains resource-intensive and…

cs.RO2026

Ontological grounding for sound and natural robot explanations via large language models

Alberto Olivares-Alarcos, Muhammad Ahsan, Satrio Sanjaya +2

Building effective human-robot interaction requires robots to derive conclusions from their experiences that are both logically sound and communicated in ways aligned with human ex…

cs.AI2026

From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)

Aniol Civit, Antonio Rago, Antonio Andriella +2

Gradual argumentation is a field of symbolic AI which is attracting attention for its ability to support transparent and contestable AI systems. It is considered a useful tool in d…

cs.RO2026★ 1 cited

HEXAR: a Hierarchical Explainability Architecture for Robots

Tamlin Love, Ferran Gebellí, Pradip Pramanick +5

As robotic systems become increasingly complex, the need for explainable decision-making becomes critical. Existing explainability approaches in robotics typically either focus on…

cs.RO2025

Multi-User Personalisation in Human-Robot Interaction: Resolving Preference Conflicts Using Gradual Argumentation

Aniol Civit, Antonio Andriella, Carles Sierra +1

While personalisation in Human-Robot Interaction (HRI) has advanced significantly, most existing approaches focus on single-user adaptation, overlooking scenarios involving multipl…

cs.RO2025★ 2 cited

Temporal Counterfactual Explanations of Behaviour Tree Decisions

Tamlin Love, Antonio Andriella, Guillem Alenyà

Explainability, in particular, the ability for robots to explain why they have made a decision or behaved in a certain way, is a critical tool in helping users understand the robot…