We also show that incorporating chain-of-thought reasoning allows RT-2 to perform multi-stage semantic reasoning, like deciding which object could be used as an improvised hammer (a rock), or which type of drink is best for a tired person (an energy drink). This includes interpreting new commands and responding to user commands by performing rudimentary reasoning, such as reasoning about object categories or high-level descriptions. RT-2 shows improved generalisation capabilities and semantic and visual understanding beyond the robotic data it was exposed to. More specifically, our work used RT-1 robot demonstration data that was collected with 13 robots over 17 months in an office kitchen environment. This work builds upon Robotic Transformer 1 (RT-1), a model trained on multi-task demonstrations, which can learn combinations of tasks and objects seen in the robotic data. A visual-language model (VLM) pre-trained on web-scale data is learning from RT-1 robotics data to become RT-2, a visual-language-action (VLA) model that can control a robot.
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