# Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation

**Authors:** Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, Tao Kong  
**arXiv:** [2312.13139](https://arxiv.org/abs/2312.13139)  
**Date:** 2023-12-20  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2312.13139v1/

## Key points

- Wu et al. explore the effectiveness of generative pre-trained models for visual robot manipulation
- Introduce GR-1, a GPT-style model designed for multi-task language-conditioned visual robot manipulation
- GR-1 takes inputs like language instructions, observation images, and robot states to predict robot actions and future images in an end-to-end manner
- Demonstrated flexibility and adaptability of GR-1 through fine-tuning on robot data after pre-training on a large-scale video dataset
- Outperformed state-of-the-art baseline methods in CALVIN benchmark and real robots experiments
- Improved success rate from 88.9% to 94.9% on CALVIN benchmark and from 53.3% to 85.4% in zero-shot unseen scene generalization
- Showed strong potential in generalizing to unseen scenes and objects in real robot experiments
- Unified GPT-style transformer augmented with large-scale video generative pre-training achieved remarkable generalization in multi-task visual robot manipulation

## Summary

In their paper titled "Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation," Wu et al. explore the effectiveness of generative pre-trained models in the context of visual robot manipulation. They introduce GR-1, a GPT-style model specifically designed for multi-task language-conditioned visual robot manipulation. This model takes inputs such as a language instruction, observation images, and robot states to predict both robot actions and future images in an end-to-end manner. The researchers demonstrate that GR-1 can be fine-tuned on robot data after being pre-trained on a large-scale video dataset, showcasing its flexibility and adaptability. Through extensive experiments on the CALVIN benchmark and real robots, they show that GR-1 outperforms state-of-the-art baseline methods. Specifically, they improve the success rate from 88.9% to 94.9% on the CALVIN benchmark and from 53.3% to 85.4% in zero-shot unseen scene generalization. Moreover, in real robot experiments, GR-1 exhibits strong potential in generalizing to unseen scenes and objects, surpassing baseline methods once again. The researchers provide compelling evidence that a unified GPT-style transformer augmented with large-scale video generative pre-training can achieve remarkable generalization in multi-task visual robot manipulation. The authors acknowledge the contributions of Ego4D and CALVIN benchmark creators as well as their colleagues at ByteDance Research for their support throughout the project. This work represents a significant advancement in leveraging pre-trained models for enhancing visual robot manipulation tasks.