Pre-training Auto-regressive Robotic Models with 4D Representations
Dantong Niu, Yuvan Sharma, Haoru Xue, Giscard Biamby, Junyi Zhang, Ziteng Ji, Trevor Darrell, Roei Herzig
2025-02-19
Summary
This paper talks about a new way to train robots called ARM4R, which uses 4D representations from human videos to make robots better at learning and performing tasks. It's like teaching robots by showing them lots of videos of humans doing things, then helping them understand and copy those actions in 3D space.
What's the problem?
While AI has made big leaps in understanding language and images, robots haven't caught up as quickly. This is because it's expensive to collect data specifically for robots, and it's hard to represent the physical world in a way that robots can understand and use effectively.
What's the solution?
The researchers created ARM4R, which takes 2D videos of humans doing tasks and turns them into 3D representations over time (making them 4D). It then uses these 4D representations to teach robots how to move and interact with objects. This method allows robots to learn from regular videos of humans, which are much easier to get than special robot training data.
Why it matters?
This matters because it could make robots much smarter and more adaptable without needing expensive, specialized training data. By learning from human videos, robots might be able to pick up new skills more quickly and perform better in different environments. This could lead to robots that are more useful in everyday life, able to handle a wider variety of tasks in homes, workplaces, and other settings.
Abstract
Foundation models pre-trained on massive unlabeled datasets have revolutionized natural language and computer vision, exhibiting remarkable generalization capabilities, thus highlighting the importance of pre-training. Yet, efforts in robotics have struggled to achieve similar success, limited by either the need for costly robotic annotations or the lack of representations that effectively model the physical world. In this paper, we introduce ARM4R, an Auto-regressive Robotic Model that leverages low-level 4D Representations learned from human video data to yield a better pre-trained robotic model. Specifically, we focus on utilizing 3D point tracking representations from videos derived by lifting 2D representations into 3D space via monocular depth estimation across time. These 4D representations maintain a shared geometric structure between the points and robot state representations up to a linear transformation, enabling efficient transfer learning from human video data to low-level robotic control. Our experiments show that ARM4R can transfer efficiently from human video data to robotics and consistently improves performance on tasks across various robot environments and configurations.