Google PaLM-E AI model enables robots to understand natural language and execute tasks

Google PaLM-E AI model enables robots to understand natural language and execute tasks

Google’s Robotics team has revealed a new artificial intelligence (AI) model that could enable robots to understand and execute instructions given in natural language. The AI model is based on Google’s existing large language model (LLM) called “PaLM.”

The platform is dubbed PaLM-E and combines vision with ChatGPT-style AI models for natural language processing (NLP). Mobile robots can observe their environment through a camera and act accordingly without needing preprocessed scene representations. In simpler terms, PaLM-E can “understand” what it sees in its environment simply by looking at it.

More technically, Google notes in its research paper that “The main architectural idea of PaLM-E is to inject continuous, embodied observations such as images, state estimates, or other sensor modalities into the language embedding space of a pre-trained language model. This is realized by encoding the continuous observations into a sequence of vectors with the same dimension as the embedding space of the language tokens.”

This allows it to understand visual information in the same way it processes language.

What makes PaLM-E remarkable is that it can react to environmental changes and complete complex multi-step tasks requiring both navigation and manipulation. For example, it could be given the instruction “I spilled my drink, can you bring me something to clean it up?” and would then plan a sequence of actions including “1. Find a sponge, 2. Pick up the sponge, 3. Bring it to the user, 4. Put down the sponge” to complete the task.

The researchers also noted that PaLM-E exhibits “positive transfer,” meaning it can take knowledge and skills acquired from prior tasks and apply them to new ones, leading to higher performance than single-task robot models. Furthermore, they found it also can analyze a sequence of inputs consisting of both language and visual information, as well as “multi-image inference,” where multiple images are used to predict something.

Additionally, they noticed that the larger the language model is, the more it maintains its language capabilities when training on visual language and robotics tasks.

“Our evaluations show that PaLM-E, a single large embodied multimodal model, can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments, and exhibits positive transfer,” concludes Google.

That said, Google Robotics isn’t the only organization exploring neural networks for robotic control. Microsoft recently released a paper called “ChatGPT for Robotics,” similar to Google’s research.

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