# Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations

**Authors:** Julen Urain, Ajay Mandlekar, Yilun Du, Mahi Shafiullah, Danfei Xu, Katerina Fragkiadaki, Georgia Chalvatzaki, Jan Peters  
**arXiv:** [2408.04380](https://arxiv.org/abs/2408.04380)  
**Date:** 2024-08-08  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2408.04380v1/

## Key points

- Learning from Demonstrations field popularity increased with deep generative models
- Traditional methods struggle with complex data distributions and scalability
- Deep generative models are being leveraged in robot learning for vast datasets
- Various types of models explored: energy-based, diffusion, action value maps, GANs
- Applications include grasp generation, trajectory generation, and cost learning
- Models can generalize beyond existing distributions
- Survey discusses strategies for enhancing generalization capabilities
- Research challenges and future directions highlighted for advancing deep generative models in robotics

## Summary

The field of Learning from Demonstrations has gained popularity with the emergence of deep generative models. Traditional methods have struggled to capture complex data distributions and scale to large numbers of demonstrations. Recently, there has been a growing interest in leveraging deep generative models within the robot learning community to handle vast datasets. A recent survey offers a comprehensive review of advancements made in utilizing these models in robotics over the past year. Different types of models have been explored, including energy-based models, diffusion models, action value maps, and generative adversarial networks. These models have found applications in grasp generation, trajectory generation, and cost learning. One crucial aspect is their ability to generalize beyond existing distributions. The survey discusses strategies for enhancing generalization capabilities and highlights research challenges and future directions for advancing the use of deep generative models in robotics. , which focuses on learning robot behavior models from data,, , , and .