# EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning

arXiv: https://arxiv.org/abs/2410.23234 · HTML: https://mlsanity.com/paper/2410.23234/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Large Language Models as General Pattern Machines](https://mlsanity.com/paper/2307.04721/) (2023)
- [Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics](https://mlsanity.com/paper/2403.19578/) (2024)
- [Prompt a Robot to Walk with Large Language Models](https://mlsanity.com/paper/2309.09969/) (2023)
- [Understanding Large-Language Model (LLM)-powered Human-Robot Interaction](https://mlsanity.com/paper/2401.03217/) (2024)
- [The GENEA Challenge 2023: A large scale evaluation of gesture generation models in monadic and dyadic settings](https://mlsanity.com/paper/2308.12646/) (2023)
- [Generative Expressive Robot Behaviors using Large Language Models](https://mlsanity.com/paper/2401.14673/) (2024)
- [Nonverbal Cues in Human-Robot Interaction: A Communication Studies Perspective](https://mlsanity.com/paper/2304.11293/) (2023)
- [Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru](https://mlsanity.com/paper/2402.11571/) (2024)

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

- [Action Tokenizer Matters in In-Context Imitation Learning](https://mlsanity.com/paper/2503.01206/) (2025)
- [The Robot's Inner Critic: Self-Refinement of Social Behaviors through VLM-based Replanning](https://mlsanity.com/paper/2603.20164/) (2026)
- [A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics](https://mlsanity.com/paper/2604.15215/) (2026)
- [Bridging Speech, Emotion, and Motion: a VLM-based Multimodal Edge-deployable Framework for Humanoid Robots](https://mlsanity.com/paper/2602.07434/) (2026)
- [Multi-Modal Multi-Agent Robotic Cognitive Alignment enabled by Non-Invasive Consumer Brain Computer Interfaces: A Proof of Concept Exploration](https://mlsanity.com/paper/2606.13190/) (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)

This paper introduces a framework, called EMOTION, for generating expressive motion sequences in humanoid robots, enhancing their ability to engage in humanlike non-verbal communication. Non-verbal cues such as facial expressions, gestures, and body movements play a crucial role in effective interpersonal interactions. Despite the advancements in robotic behaviors, existing methods often fall short in mimicking the diversity and subtlety of human non-verbal communication. To address this gap, our approach leverages the in-context learning capability of large language models (LLMs) to dynamically generate socially appropriate gesture motion sequences for human-robot interaction. We use this framework to generate 10 different expressive gestures and conduct online user studies comparing the naturalness and understandability of the motions generated by EMOTION and its human-feedback version, EMOTION++, against those by human operators. The results demonstrate that our approach either matches or surpasses human performance in generating understandable and natural robot motions under certain scenarios. We also provide design implications for future research to consider a set of variables when generating expressive robotic gestures.