Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence

Abstract

Interactive artificial intelligence in the motion control field is an interesting topic, especially when universal knowledge is adaptive to multiple tasks and universal environments. Despite there being increasing efforts in the field of Reinforcement Learning (RL) with the aid of transformers, most of them might be limited by the offline training pipeline, which prohibits exploration and generalization abilities. To address this limitation, we propose the framework of Online Decision MetaMorphFormer (ODM) which aims to achieve self-awareness, environment recognition, and action planning through a unified model architecture. Motivated by cognitive and behavioral psychology, an ODM agent is able to learn from others, recognize the world, and practice itself based on its own experience. ODM can also be applied to any arbitrary agent with a multi-joint body, located in different environments, and trained with different types of tasks using large-scale pre-trained datasets. Through the use of pre-trained datasets, ODM can quickly warm up and learn the necessary knowledge to perform the desired task, while the target environment continues to reinforce the universal policy. Extensive online experiments as well as few-shot and zero-shot environmental tests are used to verify ODM's performance and generalization ability. The results of our study contribute to the study of general artificial intelligence in embodied and cognitive fields. Code, results, and video examples can be found on the website \url{https://rlodm.github.io/odm/}.