---
license: apache-2.0
---

# This&That Model V1.1 Card

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[**Project Page**](https://cfeng16.github.io/this-and-that/) **|** [**Paper (ArXiv)**](https://arxiv.org/abs/2407.05530) **|** [**Code**](https://github.com/Kiteretsu77/This_and_That_VDM)

</div>

## Introduction

We propose a robot learning method for communicating, planning, and executing a wide range of tasks, dubbed This&That. 
We achieve robot planning for general tasks by leveraging the power of video generative models trained on
internet-scale data containing rich physical and semantic context. 
In this work, we tackle three fundamental challenges in video-based planning: 1) unambiguous task
communication with simple human instructions, 2) controllable video generation
that respects user intents, and 3) translating visual planning into robot actions. 
We propose language-gesture conditioning to generate videos, which is both simpler
and clearer than existing language-only methods, especially in complex and uncertain environments. 
We then suggest a behavioral cloning design that seamlessly incorporates the video plans. This&That demonstrates state-of-the-art effectiveness
in addressing the above three challenges, and justifies the use of video generation vas an intermediate representation for generalizable task planning and execution.






## Citation
```bibtex
@article{wang2024language,
  title={This\&That: Language-Gesture Controlled Video Generation for Robot Planning},
  author={Wang, Boyang and Sridhar, Nikhil and Feng, Chao and Van der Merwe, Mark and Fishman, Adam and Fazeli, Nima and Park, Jeong Joon},
  journal={arXiv preprint arXiv:2407.05530},
  year={2024}
}
```