The Next Wave of AI: Embodied Intelligence | Challenges and Development Stages | A1 Robot
This article is a summary of a YouTube video "【人工智能】具身智能：下一个AI浪潮 | 稚晖君 | Embodied AI | 什么是具身智能 | 目前发展阶段 | 挑战与困难 | 智元远征A1机器人" by 最佳拍档
TLDR Embodied intelligence aims to achieve understanding, interaction, and task completion through learning and evolution in both the physical and digital worlds, and Google is leading the way in research on this topic.
Embodied intelligence aims to achieve understanding, interaction, and task completion through learning and evolution in both the physical and digital worlds.
Embodied AI involves agents that can perceive and interact with complex environments, making decisions based on changes in the environment and target states to complete tasks.
Google is leading the way in research on embodied intelligence, with multiple technical routes and continuous upgrades, thanks to its AI research institutions, Google Brain and DeepMind.
The RT-2 series models show the ability to complete emerging tasks such as symbol understanding, reasoning, and human recognition with high accuracy, indicating the transfer of semantic knowledge from visual language data.
Embodied intelligence requires real-time perception and decision-making abilities to adapt to complex and changing environments, posing challenges in terms of high-speed data collection, transmission, processing, and the consumption of computing power.
The ability for embodied intelligence to continuously learn and evolve through interactions, both virtual and real, is crucial for its development and adaptation to changing environments.
The model of embodied AI aims to combine the information from various sensors, similar to how the human brain combines sensory inputs to generate motion commands.
The success of embodied AI relies on the ability of the upper-level cloud supercomputer and brain to accurately perceive and make decisions for new tasks and environments.