In this article, we’ll look at the groundbreaking work of Google and the Technical University of Berlin in developing an AI-powered robot brain called PaLM-E.
Key Takeaways:
This groundbreaking technology can perform various tasks without requiring individual retraining, thanks to its distinctive blend of vision and language integration.
PaLM-E is an AI-powered robot brain that can receive high-level commands like “fetch me the rice chips” and create a plan of action for a mobile robot with an arm.
Developed by Google Robotics, PaLM-E uses data from the robot’s camera to analyze the task and carry out the actions without the need for pre-processing, making it incredibly autonomous..
One of the most exciting features of PaLM-E is its resilience, allowing it to react to its environment and guide other robots.
For example, PaLM-E can guide a robot to retrieve a bag of chips from a kitchen and is resistant to interruptions that may occur during the task.
In addition, the PaLM-E model can autonomously control a robot through complex sequences that previously required human guidance.
PaLM-E uses Google’s pre-existing large language model called PaLM, which is similar to the technology used in ChatGPT.
By encoding continuous observations like images or sensor data into a sequence of vectors that are the same size as language tokens, PaLM-E can “understand” sensory information in the same way it processes language.
PaLM-E is inspired by Google’s previous work on ViT-22B, a vision transformer model that was trained to perform a variety of visual tasks like image classification, object detection, semantic segmentation, and image captioning.
This combination of vision and language integration allows PaLM-E to perform a wide range of tasks without requiring retraining.
PaLM-E’s remarkable feature is its capability to transfer knowledge and skills from one task to another, leading to much better performance compared to single-task robots.
The larger the language model, the better it retains its language capabilities when training on visual-language and robotics tasks.
The 562B PaLM-E model can almost maintain all of its language capabilities.
According to the researchers, PaLM-E has advanced abilities such as multimodal chain-of-thought reasoning, which enables the model to evaluate a sequence of inputs consisting of both language and visual information.
Furthermore, PaLM-E can make predictions or inferences based on multiple images, even though it was only trained on single-image prompts.
Google researchers aim to investigate additional uses of PaLM-E in real-life situations like industrial robotics or home automation.
They trust that PaLM-E will encourage more research on embodied AI and multimodal reasoning, leading to the advancement of artificial general intelligence that can carry out tasks like humans.
Overall, the development of PaLM-E represents a significant step forward in the integration of vision and language for robotic control.
Its ability to perform multiple tasks without retraining and transfer knowledge and skills from one task to another makes it an exciting technology for future applications in various industries.
As research continues to push the boundaries of AI, the possibilities for PaLM-E and other advanced technologies are endless.
