# Generative Expressive Robot Behaviors using Large Language Models

arXiv: https://arxiv.org/abs/2401.14673 · HTML: https://mlsanity.com/paper/2401.14673/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2024.

## Built on by (most cited first, 22 of 22)

- [VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots](https://mlsanity.com/paper/2404.04066/) (2024)
- [To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions](https://mlsanity.com/paper/2403.12533/) (2024)
- [MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts](https://mlsanity.com/paper/2403.06041/) (2024)
- [ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics](https://mlsanity.com/paper/2411.18825/) (2024)
- [EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning](https://mlsanity.com/paper/2410.23234/) (2024)
- [A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards](https://mlsanity.com/paper/2502.08643/) (2025)
- [Normative Modules: A Generative Agent Architecture for Learning Norms that Supports Multi-Agent Cooperation](https://mlsanity.com/paper/2405.19328/) (2024)
- [Foundation Models for Autonomous Robots in Unstructured Environments](https://mlsanity.com/paper/2407.14296/) (2024)
- [Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approach](https://mlsanity.com/paper/2410.17602/) (2024)
- [Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review](https://mlsanity.com/paper/2505.20503/) (2025)
- [Towards an LLM-Based Speech Interface for Robot-Assisted Feeding](https://mlsanity.com/paper/2410.20624/) (2024)
- [Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models](https://mlsanity.com/paper/2501.00953/) (2025)
- [LAMS: LLM-Driven Automatic Mode Switching for Assistive Teleoperation](https://mlsanity.com/paper/2501.08558/) (2025)
- [Efficiently Generating Expressive Quadruped Behaviors via Language-Guided Preference Learning](https://mlsanity.com/paper/2502.03717/) (2025)
- [GenComUI: Exploring Generative Visual Aids as Medium to Support Task-Oriented Human-Robot Communication](https://mlsanity.com/paper/2502.10678/) (2025)
- [Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models](https://mlsanity.com/paper/2506.14727/) (2025)
- [SIL: Symbiotic Interactive Learning for Language-Conditioned Human-Agent Co-Adaptation](https://mlsanity.com/paper/2511.05203/) (2025)
- [The Robot's Inner Critic: Self-Refinement of Social Behaviors through VLM-based Replanning](https://mlsanity.com/paper/2603.20164/) (2026)
- [ELLA: Generative AI-Powered Social Robots for Early Language Development at Home](https://mlsanity.com/paper/2603.12508/) (2026)
- [Towards Considerate Human-Robot Coexistence: A Dual-Space Framework of Robot Design and Human Perception in Healthcare](https://mlsanity.com/paper/2604.04374/) (2026)
- [Learning Transferable Latent User Preferences for Human-Aligned Decision Making](https://mlsanity.com/paper/2605.12682/) (2026)
- [Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs](https://mlsanity.com/paper/2606.18747/) (2026)

## Abstract (arXiv)

People employ expressive behaviors to effectively communicate and coordinate their actions with others, such as nodding to acknowledge a person glancing at them or saying "excuse me" to pass people in a busy corridor. We would like robots to also demonstrate expressive behaviors in human-robot interaction. Prior work proposes rule-based methods that struggle to scale to new communication modalities or social situations, while data-driven methods require specialized datasets for each social situation the robot is used in. We propose to leverage the rich social context available from large language models (LLMs) and their ability to generate motion based on instructions or user preferences, to generate expressive robot motion that is adaptable and composable, building upon each other. Our approach utilizes few-shot chain-of-thought prompting to translate human language instructions into parametrized control code using the robot's available and learned skills. Through user studies and simulation experiments, we demonstrate that our approach produces behaviors that users found to be competent and easy to understand. Supplementary material can be found at https://generative-expressive-motion.github.io/.