# Learning Universal Policies via Text-Guided Video Generation

**Authors:** Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, Pieter Abbeel  
**arXiv:** [2302.00111](https://arxiv.org/abs/2302.00111)  
**Date:** 2023-01-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2302.00111v3/

## Key points

- Significant progress in artificial intelligence, particularly in text-guided image synthesis
- Development of models capable of generating complex and novel images by leveraging text descriptions
- Exploration of using tools to create general-purpose agents for solving a wide range of tasks
- Policy-as-video formulation for natural and combinatorial generalization to novel tasks
- Utilization of text and images as universal interfaces in policy learning for knowledge preservation
- Diffusion modeling enabling long-term planning and hierarchical decision-making
- Effectiveness of representing policies using text-conditioned video generation for achieving generalization, multi-task learning, and real-world transferability

## Summary

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Significant progress has been made in the field of artificial intelligence, particularly in text-guided image synthesis. This has led to the development of models capable of generating complex and novel images by leveraging text descriptions. Researchers are now exploring the potential of using these tools to create more general-purpose agents that can solve a wide range of tasks. One approach being investigated involves casting sequential decision-making as a text-conditioned video generation task. In this framework, a planner synthesizes future frames based on a text-encoded specification of a desired goal, depicting planned actions for achieving the specified goal. By utilizing text as the underlying goal specification, this approach enables natural and combinatorial generalization to novel tasks. The proposed policy-as-video formulation offers several advantages, including representation of environments with different state and action spaces in a unified space of images, facilitating learning and generalization across various robot manipulation tasks. Additionally, knowledge transfer is enabled through predicting highly realistic video plans for real robots using pretrained language embeddings and publicly available videos from the internet. Furthermore, this work builds upon previous research on large-scale pretraining in vision and language domains to develop generalist decision-making agents. Unlike existing approaches that rely on customized tokens or operate within specific environments with identical state and action spaces, using text and images as universal interfaces in policy learning preserves knowledge from pretrained vision and language models. Additionally, employing diffusion modeling instead of autoregressive sequence modeling enables long-term planning and hierarchical decision-making. In conclusion, this study demonstrates the effectiveness of representing policies using text-conditioned video generation for achieving combinatorial generalization, multi-task learning, and real-world transferability. While there are limitations such as slow video diffusion processes and challenges in partially observable environments, future work may focus on optimizing speed through sampling networks and integrating semantic knowledge into video models to address these issues. Overall, this research highlights the potential of generative models and internet data for developing versatile decision-making systems with broad applicability across diverse tasks and environments.