Abstract: This talk introduces two novel approaches to low-level embodied intelligence through integrating large language models (LLMs) with robotics, focusing on "Language to Reward" and "Robotics Transformer(RT)-2". The former employs LLMs to generate reward code, creating a bridge between high-level language instructions and low-level robotic actions. This method allows for real-time user interaction, efficiently controlling robotic arms for various tasks and outperforming baseline methodologies. "RT-2" integrates advanced vision-language models with robotic control by co-fine-tuning on robotic trajectory data and extensive web-based vision-language tasks, resulting in the robust RT-2 model which exhibits strong generalization capabilities. This approach allows robots to execute untrained commands and efficiently perform multi-stage semantic reasoning tasks, exemplifying significant advancements in contextual understanding and response to user commands. These projects demonstrate that language models can extend beyond their conventional domain of high-level reasoning tasks, playing a crucial role not only in interpreting and generating instructions but also in the nuanced generation of low-level robotic actions.
Bio: Fei Xia is a Senior Research Scientist at Google DeepMind where he works on the Robotics team. He received his PhD degree from the Department of Electrical Engineering, Stanford University. He was co-advised by Silvio Savarese in SVL and Leonidas Guibas. His mission is to build intelligent embodied agents that can interact with complex and unstructured real-world environments. His research has been awarded a CoRL Special Innovation award and an ICRA Outstanding Robot Learning Paper Award, and featured on popular media outlets such as New York Times, Reuters, and WIRED. Most recently, He has been exploring using foundation models for robot decision making.
