Google’s DeepMind research team has made significant progress in robotics research with the introduction of Open X-Embodiment, a database of robotics functionality created in collaboration with 33 research institutes. The team compared this new system to ImageNet, a widely-used landmark database in computer vision research. Open X-Embodiment aims to train a generalist model capable of controlling various types of robots, following diverse instructions, performing complex tasks, and effectively generalizing its knowledge.
The database currently includes over 500 skills and 150,000 tasks gathered from 22 different robot embodiments. DeepMind then trained its RT-1-X model on this data and successfully used it to train robots in other labs, achieving a 50% success rate compared to in-house methods.
Vincent Vanhoucke, the head of robotics at Google DeepMind, shared his perspective on the development of robotics research. He highlighted the increasing capability of perception technology, such as computer vision and audio processing, and its impact on the possibility of real-world robotics. Vanhoucke emphasized the necessity of strong perception for robots to operate and perform tasks effectively in everyday environments.
Regarding the integration of Everyday Robots, a robotics-focused team within Google, into DeepMind, Vanhoucke explained that collaboration between the teams has been ongoing for seven years. He discussed a skunkworks project where they worked together to explore machine learning and perception-based control of robotic grasping. This project marked a turning point in the understanding of how AI and machine learning could solve problems in robotics.
The inclusion of Everyday Robots’ work in the DeepMind robotics team reflects the belief that the general AI problem encompasses the challenges of robotics. DeepMind has shifted its focus towards solving perception, understanding, and control in the context of general AI to drive advancements in real-world robotics.
Q: What is Open X-Embodiment?
A: Open X-Embodiment is a database of robotics functionality developed by Google’s DeepMind research team in collaboration with 33 research institutes. It aims to train a generalist model capable of controlling different types of robots and performing a variety of tasks.
Q: What is the purpose of Open X-Embodiment?
A: The purpose of Open X-Embodiment is to advance robotics research by providing a repository of diverse robot demonstrations. It serves as a resource to train robots to perform complex tasks, follow instructions, and generalize effectively.
Q: What is the significance of DeepMind’s RT-1-X model?
A: DeepMind’s RT-1-X model is trained on the Open X-Embodiment data and has shown a 50% success rate in training robots in other labs. It showcases the potential of the generalist model for controlling robots and highlights the effectiveness of the Open X-Embodiment database.
Q: How has collaboration between Everyday Robots and DeepMind contributed to robotics research?
A: Collaboration between Everyday Robots and DeepMind has been ongoing for seven years. The teams have worked together on projects exploring machine learning and perception-based control of robotic grasping. This collaboration has paved the way for advancements in solving problems in robotics using AI and machine learning techniques.