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Generative AI has opened up new ways to train robots and accelerated the development of the industry.
The field of robotics has been undergoing dramatic changes in recent months, thanks in large part to the rapid development of generative artificial intelligence. Major technology companies and research laboratories use generative AI models to solve key problems in robotics. VentureBeat has covered some of the innovative ways that generative AI can help advance robotics research.
Bridging the gap between simulation and reality
Training robots in real-world environments faces many challenges: it's expensive, slow, and limited access to a variety of environments. In response, researchers use simulations. However, creating detailed virtual environments requires significant resources and money. There is also the problem of the "gap between simulation and reality", when models trained in a virtual environment cannot cope with real conditions.
Generative models have become key tools for bridging this gap. For example, Nvidia uses the NeRF model to create realistic 3D environments from video.
Other models also play an important role in improving virtual environments. For example, SyncDreamer generates multiple representations of an object from a single 2D image, and DeepMind's UniSim creates photorealistic video sequences that can be used to create detailed simulations for training robotic models.,
Improved interaction between robots and humans
Improving human-robot communication remains an important task. A prime example is Google's PaLM-E language model. The model combines language models and visual converters that are jointly trained to understand correlations between images and text.
The model then applies this knowledge to analyze visual scenes and translate natural language instructions into robot actions. Models like the PaLM-E have significantly improved robots ' ability to execute complex commands.
Integration of heterogeneous data sets
A lot of data from different robots makes it necessary to combine them. For example, a joint project between DeepMind and 33 RT-X research institutes combined data from 22 robots and 20 institutions. The data set included 500 skills and 150,000 tasks.
The ambitious goal of the project is to develop a general-purpose AI system that can work with various types of physical robots and perform a wide range of tasks. The project was inspired by work on Large Language Models (LLM), which shows that LLM training on very large data sets can allow you to perform tasks that were previously unavailable.
Creating better reward models
Generative models are widely used in writing code, and interestingly, they can also generate code for training robots. Nvidia's latest Eureka model uses generative AI to develop reward simulations — a notoriously complex component of the reinforcement learning systems used to train robots.
Eureka uses GPT-4 to write code, eliminating the need for task-specific prompts or predefined reward templates. AI uses simulation environments and GPUs to quickly assess the quality of a large number of reward candidates, thereby optimizing the learning process. Eureka is also able to analyze and improve the generated code. Moreover, AI can take into account people's feedback to improve the reward model and more accurately align it with the developer's goals.
Generative models that started with simple tasks are now being applied to much more complex areas. With the development of generative AI in robotics, we can expect even more innovations and more widespread use of robots in everyday life.
The field of robotics has been undergoing dramatic changes in recent months, thanks in large part to the rapid development of generative artificial intelligence. Major technology companies and research laboratories use generative AI models to solve key problems in robotics. VentureBeat has covered some of the innovative ways that generative AI can help advance robotics research.
Bridging the gap between simulation and reality
Training robots in real-world environments faces many challenges: it's expensive, slow, and limited access to a variety of environments. In response, researchers use simulations. However, creating detailed virtual environments requires significant resources and money. There is also the problem of the "gap between simulation and reality", when models trained in a virtual environment cannot cope with real conditions.
Generative models have become key tools for bridging this gap. For example, Nvidia uses the NeRF model to create realistic 3D environments from video.
Other models also play an important role in improving virtual environments. For example, SyncDreamer generates multiple representations of an object from a single 2D image, and DeepMind's UniSim creates photorealistic video sequences that can be used to create detailed simulations for training robotic models.,
Improved interaction between robots and humans
Improving human-robot communication remains an important task. A prime example is Google's PaLM-E language model. The model combines language models and visual converters that are jointly trained to understand correlations between images and text.
The model then applies this knowledge to analyze visual scenes and translate natural language instructions into robot actions. Models like the PaLM-E have significantly improved robots ' ability to execute complex commands.
Integration of heterogeneous data sets
A lot of data from different robots makes it necessary to combine them. For example, a joint project between DeepMind and 33 RT-X research institutes combined data from 22 robots and 20 institutions. The data set included 500 skills and 150,000 tasks.
The ambitious goal of the project is to develop a general-purpose AI system that can work with various types of physical robots and perform a wide range of tasks. The project was inspired by work on Large Language Models (LLM), which shows that LLM training on very large data sets can allow you to perform tasks that were previously unavailable.
Creating better reward models
Generative models are widely used in writing code, and interestingly, they can also generate code for training robots. Nvidia's latest Eureka model uses generative AI to develop reward simulations — a notoriously complex component of the reinforcement learning systems used to train robots.
Eureka uses GPT-4 to write code, eliminating the need for task-specific prompts or predefined reward templates. AI uses simulation environments and GPUs to quickly assess the quality of a large number of reward candidates, thereby optimizing the learning process. Eureka is also able to analyze and improve the generated code. Moreover, AI can take into account people's feedback to improve the reward model and more accurately align it with the developer's goals.
Generative models that started with simple tasks are now being applied to much more complex areas. With the development of generative AI in robotics, we can expect even more innovations and more widespread use of robots in everyday life.
