ByteDance Unveils Robot Model with World Modeling and Strong Generalization

ByteDance's GR-2 robot model excels in multi-task learning and generalization, unlocking potential for robotics in real-world applications.

Recently, ByteDance Research released an official video and technical report for its second-generation robot large model, GR-2.

GR-2, with its excellent generalization capability and multi-task versatility, signals that robotic large models will unleash immense potential and endless possibilities.

Introduction to GR-2: Refined Through Trials

Like many large models, GR-2's training involves two phases: pre-training and fine-tuning.

If we compare robots to humans, the pre-training process is like a "baby phase" for humans. GR-2's baby phase, however, is quite different from other robots.

During pre-training, GR-2 navigated the vast expanse of the internet.

It underwent generative training on 38 million internet video clips, earning its name GR-2 (Generative Robot 2.0).

These videos came from publicly available academic datasets, covering various everyday human activities in different settings such as homes, outdoors, and offices.

This process was akin to GR-2 experiencing rapid "growing pains," quickly learning a wide range of human actions and behavior patterns in daily life.

This pre-training method endowed GR-2 with the potential to learn various operational tasks and generalize across different environments. Its vast knowledge base gives GR-2 a deep understanding of the world as if it has traveled the globe countless times.