# Enhancing Social Robots through Resilient AI

arXiv: https://arxiv.org/abs/2510.21469 · HTML: https://mlsanity.com/paper/2510.21469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Measure and Improve Robustness in NLP Models: A Survey](https://mlsanity.com/paper/2112.08313/) (2021)
- [Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2201.00768/) (2022)
- [Exploring Vulnerabilities and Protections in Large Language Models: A Survey](https://mlsanity.com/paper/2406.00240/) (2024)
- [Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2310.08565/) (2023)
- [RAILS: A Robust Adversarial Immune-inspired Learning System](https://mlsanity.com/paper/2107.02840/) (2021)
- [Cooperative Resilience in Artificial Intelligence Multiagent Systems](https://mlsanity.com/paper/2409.13187/) (2024)
- [Resilient robot teams: a review integrating decentralised control, change-detection, and learning](https://mlsanity.com/paper/2204.10063/) (2022)
- [Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning](https://mlsanity.com/paper/2409.10655/) (2024)

## Abstract (arXiv)

As artificial intelligence continues to advance and becomes more integrated into sensitive areas like healthcare, education, and everyday life, it's crucial for these systems to be both resilient and robust. This paper shows how resilience is a fundamental characteristic of social robots, which, through it, ensure trust in the robot itself-an essential element especially when operating in contexts with elderly people, who often have low trust in these systems. Resilience is therefore the ability to operate under adverse or stressful conditions, even when degraded or weakened, while maintaining essential operational capabilities.