  A fundamental objective in robot manipulation is to enable models to
comprehend visual scenes and execute actions. Although existing robot
Multimodal Large Language Models (MLLMs) can handle a range of basic tasks,
they still face challenges in two areas: 1) inadequate reasoning ability to
tackle complex tasks, and 2) high computational costs for MLLM fine-tuning and
inference. The recently proposed state space model (SSM) known as Mamba
demonstrates promising capabilities in non-trivial sequence modeling with
linear inference complexity. Inspired by this, we introduce RoboMamba, an
end-to-end robotic MLLM that leverages the Mamba model to deliver both robotic
reasoning and action capabilities, while maintaining efficient fine-tuning and
inference. Specifically, we first integrate the vision encoder with Mamba,
aligning visual data with language embedding through co-training, empowering
our model with visual common sense and robot-related reasoning. To further
equip RoboMamba with action pose prediction abilities, we explore an efficient
fine-tuning strategy with a simple policy head. We find that once RoboMamba
possesses sufficient reasoning capability, it can acquire manipulation skills
with minimal fine-tuning parameters (0.1\% of the model) and time (20 minutes).
In experiments, RoboMamba demonstrates outstanding reasoning capabilities on
general and robotic evaluation benchmarks. Meanwhile, our model showcases
impressive pose prediction results in both simulation and real-world
experiments, achieving inference speeds 7 times faster than existing robot
MLLMs. Our project web page: https://sites.google.com/view/robomamba-web
