# Teaching Robots to Span the Space of Functional Expressive Motion

arXiv: https://arxiv.org/abs/2203.02091 · HTML: https://mlsanity.com/paper/2203.02091/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Diverse mini-batch Active Learning](https://mlsanity.com/paper/1901.05954/) (2019)
- [A survey of robot learning from demonstrations for Human-Robot Collaboration](https://mlsanity.com/paper/1710.08789/) (2017)
- [Expressing Robot Incapability](https://mlsanity.com/paper/1810.08167/) (2018)
- [Animated Cassie: A Dynamic Relatable Robotic Character](https://mlsanity.com/paper/2009.02846/) (2020)
- [Rapid Trajectory Optimization Using C-FROST with Illustration on a Cassie-Series Dynamic Walking Biped](https://mlsanity.com/paper/1807.06614/) (2018)
- [Cost Functions for Robot Motion Style](https://mlsanity.com/paper/1809.00092/) (2018)

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

- [SIRL: Similarity-based Implicit Representation Learning](https://mlsanity.com/paper/2301.00810/) (2023)
- [Aligning Robot and Human Representations](https://mlsanity.com/paper/2302.01928/) (2023)
- [CIVIL: Causal and Intuitive Visual Imitation Learning](https://mlsanity.com/paper/2504.17959/) (2025)
- [Aligning Robot Representations with Humans](https://mlsanity.com/paper/2205.07882/) (2022)
- [QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents](https://mlsanity.com/paper/2511.17855/) (2025)
- [Bridging Speech, Emotion, and Motion: a VLM-based Multimodal Edge-deployable Framework for Humanoid Robots](https://mlsanity.com/paper/2602.07434/) (2026)

## Abstract (arXiv)

Our goal is to enable robots to perform functional tasks in emotive ways, be it in response to their users' emotional states, or expressive of their confidence levels. Prior work has proposed learning independent cost functions from user feedback for each target emotion, so that the robot may optimize it alongside task and environment specific objectives for any situation it encounters. However, this approach is inefficient when modeling multiple emotions and unable to generalize to new ones. In this work, we leverage the fact that emotions are not independent of each other: they are related through a latent space of Valence-Arousal-Dominance (VAD). Our key idea is to learn a model for how trajectories map onto VAD with user labels. Considering the distance between a trajectory's mapping and a target VAD allows this single model to represent cost functions for all emotions. As a result 1) all user feedback can contribute to learning about every emotion; 2) the robot can generate trajectories for any emotion in the space instead of only a few predefined ones; and 3) the robot can respond emotively to user-generated natural language by mapping it to a target VAD. We introduce a method that interactively learns to map trajectories to this latent space and test it in simulation and in a user study. In experiments, we use a simple vacuum robot as well as the Cassie biped.