


Deep learning giant DeepMind has made breakthrough progress on the ImageNet dataset, bringing a new milestone to robotics research
Why does robotics lag so far behind natural language processing (NLP), vision and other artificial intelligence fields? Among other difficulties, data shortage is the main reason. In order to solve this problem, Google DeepMind and other institutions launched the open X-Embodiment data set and successfully trained a more powerful RT-X model





Specifically, they used RT-1 to train RT-1-X, where RT-1 is a 35M parameter network built on the Transformer architecture and is designed for robot control The design is shown in Figure 3.
The average success rate of RT-1-X is 50% higher than the original method.

In order to study the knowledge transfer capabilities of RT-X, DeepMind conducted other experiments. These experiments involved objects and skills that were not present in the RT-2 dataset, but were present in another robot's dataset. Results showed that the RT-2-X was three times more successful at mastering new skills than its previous best model, the RT-2. This also illustrates that joint training with data from other platforms can give RT-2-X additional skills not present in the original data set, allowing it to perform novel tasks.

RT-2-X shows that incorporating data from other robots into RT-2-X training can improve the robot's task performance range, but only if the use of high enough Capacity architecture.
Research inspiration: Robots need to learn from each other, and so do researchers
If you want to know more about RT-X, you can refer to this paper published by DeepMind:

- Reference link: https://www.deepmind.com/blog/scaling -up-learning-across-many-different-robot-types
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