# Nadine: An LLM-driven Intelligent Social Robot with Affective Capabilities and Human-like Memory

arXiv: https://arxiv.org/abs/2405.20189 · HTML: https://mlsanity.com/paper/2405.20189/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [Understanding Large-Language Model (LLM)-powered Human-Robot Interaction](https://mlsanity.com/paper/2401.03217/) (2024)
- [Large Language Model-based Human-Agent Collaboration for Complex Task Solving](https://mlsanity.com/paper/2402.12914/) (2024)
- [LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model](https://mlsanity.com/paper/2305.19352/) (2023)
- [Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario](https://mlsanity.com/paper/2006.13615/) (2020)
- [LLM-MARS: Large Language Model for Behavior Tree Generation and NLP-enhanced Dialogue in Multi-Agent Robot Systems](https://mlsanity.com/paper/2312.09348/) (2023)
- [Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction](https://mlsanity.com/paper/2402.15368/) (2024)
- [BTGenBot: Behavior Tree Generation for Robotic Tasks with Lightweight LLMs](https://mlsanity.com/paper/2403.12761/) (2024)
- [VITA: A Multi-modal LLM-based System for Longitudinal, Autonomous, and Adaptive Robotic Mental Well-being Coaching](https://mlsanity.com/paper/2312.09740/) (2023)
- [To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions](https://mlsanity.com/paper/2403.12533/) (2024)
- [A Sign Language Recognition System with Pepper, Lightweight-Transformer, and LLM](https://mlsanity.com/paper/2309.16898/) (2023)

## Built on by (most cited first, 6 of 6)

- [Project Riley: Multimodal Multi-Agent LLM Collaboration with Emotional Reasoning and Voting](https://mlsanity.com/paper/2505.20521/) (2025)
- [ProAct: Harnessing Streaming Motion Generation and Agentic Reasoning for Real-Time Embodied Social Interaction](https://mlsanity.com/paper/2602.14048/) (2026)
- [CAPE: A Chinese Dataset for Appraisal-based Emotional Generation using Large Language Models](https://mlsanity.com/paper/2410.14145/) (2024)
- [AI-Driven Agents with Prompts Designed for High Agreeableness Increase the Likelihood of Being Mistaken for a Human in the Turing Test](https://mlsanity.com/paper/2411.13749/) (2024)
- [Human-Inspired Context-Selective Multimodal Memory for Social Robots](https://mlsanity.com/paper/2604.12081/) (2026)
- [Exploration of Foundation Model-Based Robots in Patient and Elderly Care](https://mlsanity.com/paper/2606.10208/) (2026)

## Abstract (arXiv)

In this work, we describe our approach to developing an intelligent and robust social robotic system for the Nadine social robot platform. We achieve this by integrating Large Language Models (LLMs) and skilfully leveraging the powerful reasoning and instruction-following capabilities of these types of models to achieve advanced human-like affective and cognitive capabilities. This approach is novel compared to the current state-of-the-art LLM-based agents which do not implement human-like long-term memory or sophisticated emotional appraisal. The naturalness of social robots, consisting of multiple modules, highly depends on the performance and capabilities of each component of the system and the seamless integration of the components. We built a social robot system that enables generating appropriate behaviours through multimodal input processing, bringing episodic memories accordingly to the recognised user, and simulating the emotional states of the robot induced by the interaction with the human partner. In particular, we introduce an LLM-agent frame for social robots, SoR-ReAct, serving as a core component for the interaction module in our system. This design has brought forth the advancement of social robots and aims to increase the quality of human-robot interaction.