# Pre-training Auto-regressive Robotic Models with 4D Representations

**Authors:** Dantong Niu, Yuvan Sharma, Haoru Xue, Giscard Biamby, Junyi Zhang, Ziteng Ji, Trevor Darrell, Roei Herzig  
**arXiv:** [2502.13142](https://arxiv.org/abs/2502.13142)  
**Date:** 2025-02-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2502.13142v1/

## Key points

- Foundation models pre-trained on vast unlabeled datasets have revolutionized natural language processing and computer vision.
- Robotics faces challenges in achieving similar success due to high costs of annotating data and inadequate representations.
- ARM4R is an innovative Auto-regressive Robotic Model leveraging low-level 4D Representations from human video data.
- ARM4R uses 3D point tracking representations extracted from videos through monocular depth estimation to enhance robotic control tasks.
- ARM4R enables efficient transfer learning from human video data to robotics, demonstrating remarkable proficiency in experiments.
- The research highlights the potential of leveraging human video data for pre-training robotic models and advancing autonomous robotics.

## Summary

In the realm of artificial intelligence, foundation models pre-trained on vast unlabeled datasets have sparked a revolution in natural language processing and computer vision. These models have demonstrated exceptional generalization capabilities, underscoring the critical role of pre-training in enhancing performance across various tasks. However, the field of robotics has faced challenges in achieving similar levels of success due to high costs associated with annotating robotic data or inadequate existing representations to effectively capture complexities of the physical world. In response to these challenges, a team of researchers led by Dantong Niu, Yuvan Sharma, Haoru Xue, Giscard Biamby, Junyi Zhang, Ziteng Ji, Trevor Darrell and Roei Herzig introduces ARM4R - an innovative Auto-regressive Robotic Model that leverages low-level 4D Representations derived from human video data. The core idea behind ARM4R is to harness 3D point tracking representations extracted from videos by elevating 2D representations into three-dimensional space through monocular depth estimation over time. These 4D representations maintain a consistent geometric structure between the points and robot state representations up to a linear transformation. By capitalizing on this novel approach, ARM4R facilitates efficient transfer learning from human video data to low-level robotic control tasks. Through rigorous experiments and evaluations, the researchers demonstrate that ARM4R exhibits remarkable proficiency in transferring knowledge from human video data to robotics. Moreover, ARM4R consistently enhances performance across diverse robot environments and configurations. Overall,this groundbreaking research not only sheds light on the potential for leveraging human video data for pre-training robotic models but also paves the way for future advancements in autonomous robotics through enhanced learning mechanisms and representation techniques.