  Robotic behavior synthesis, the problem of understanding multimodal inputs
and generating precise physical control for robots, is an important part of
Embodied AI. Despite successes in applying multimodal large language models for
high-level understanding, it remains challenging to translate these conceptual
understandings into detailed robotic actions while achieving generalization
across various scenarios. In this paper, we propose a tree-structured
multimodal code generation framework for generalized robotic behavior
synthesis, termed RoboCodeX. RoboCodeX decomposes high-level human instructions
into multiple object-centric manipulation units consisting of physical
preferences such as affordance and safety constraints, and applies code
generation to introduce generalization ability across various robotics
platforms. To further enhance the capability to map conceptual and perceptual
understanding into control commands, a specialized multimodal reasoning dataset
is collected for pre-training and an iterative self-updating methodology is
introduced for supervised fine-tuning. Extensive experiments demonstrate that
RoboCodeX achieves state-of-the-art performance in both simulators and real
robots on four different kinds of manipulation tasks and one navigation task.
