Google’s DeepMind team has recently unveiled Open X-Embodiment, a revolutionary database of robotics functionality created in collaboration with 33 research institutes. This innovative system, likened to the renowned ImageNet database, aims to advance robotics and promote the development of general-purpose robots.
Open X-Embodiment currently features over 500 skills and 150,000 tasks collected from 22 different robot embodiments. While it may not boast the same scale as ImageNet in terms of sheer volume, it represents a promising start. DeepMind has already trained its RT-1-X model on this extensive dataset and successfully deployed robots in various labs, achieving a remarkable 50% success rate compared to in-house methods.
These advancements highlight the exciting potential for robotic learning. The era of bespoke robots may not be over, but the emergence of general-purpose robots is increasingly attainable. However, simulation and artificial intelligence, particularly the generative variety, will play crucial roles in this evolution. Although some companies may have prioritized hardware development without fully considering the necessary software components, the future may yet see remarkable achievements in building hardware for general tasks.
Vincent Vanhoucke, the head of robotics at Google DeepMind, recently shed light on the company’s robotics research and ambitions. While he may be relatively new to the role, having joined in May, his 16-year tenure at Google and experience as a distinguished scientist for Google AI Robotics make him an ideal candidate to discuss Google’s robotic ventures.
When asked about the history of DeepMind’s robotics team, Vanhoucke emphasized that the team originated from Google Research, which later merged with DeepMind. As perception technology, such as computer vision and audio processing, advanced to near-human levels, the prospect of real-world robotics became increasingly feasible. Vanhoucke recognized the importance of strong perception for robots to function effectively in everyday environments, prompting him to pivot towards robotics as the next phase of research.
Regarding the Everyday Robots team and Google’s previous acquisitions in the field of robotics, Vanhoucke clarified that a significant number of team members had indeed come from those acquired companies. He emphasized the team’s belief that solving the general AI problem was integral to tackling the challenges of real-world robotics. As a result, their focus shifted towards perception, understanding, and control within the context of general AI.
DeepMind’s collaboration with the Everyday Robots team over the years has been instrumental in driving progress. Vanhoucke recounted a significant milestone where they repurposed discontinued robot arms to explore the concept of machine learning and perception in grasping objects. The success of this endeavor sparked a realization that the application of machine learning and AI to robotic grasping was a novel and promising approach.
DeepMind’s Open X-Embodiment represents a significant step forward in the development of general-purpose robots. By harnessing the power of extensive datasets and leveraging advancements in AI, the possibilities for robotics are expanding rapidly. As research continues to push boundaries, we can expect to witness a future where robots possess a wide range of skills and perform complex tasks with ease.
Q: How many skills and tasks are currently in Open X-Embodiment?
A: Open X-Embodiment contains over 500 skills and 150,000 tasks.
Q: What is the success rate of DeepMind’s RT-1-X model trained on the dataset?
A: The RT-1-X model achieved a 50% success rate using the Open X-Embodiment dataset.
Q: What research institutions collaborated with DeepMind on Open X-Embodiment?
A: Open X-Embodiment was developed in collaboration with 33 research institutes.
Q: What is the main focus of DeepMind’s robotics team?
A: DeepMind’s robotics team is primarily focused on advancing general AI capabilities in the context of robotics.