  Robotic vision applications often necessitate a wide range of visual
perception tasks, such as object detection, segmentation, and identification.
While there have been substantial advances in these individual tasks,
integrating specialized models into a unified vision pipeline presents
significant engineering challenges and costs. Recently, Multimodal Large
Language Models (MLLMs) have emerged as novel backbones for various downstream
tasks. We argue that leveraging the pre-training capabilities of MLLMs enables
the creation of a simplified framework, thus mitigating the need for
task-specific encoders. Specifically, the large-scale pretrained knowledge in
MLLMs allows for easier fine-tuning to downstream robotic vision tasks and
yields superior performance. We introduce the RoboLLM framework, equipped with
a BEiT-3 backbone, to address all visual perception tasks in the ARMBench
challenge-a large-scale robotic manipulation dataset about real-world warehouse
scenarios. RoboLLM not only outperforms existing baselines but also
substantially reduces the engineering burden associated with model selection
and tuning. The source code is publicly available at
https://github.com/longkukuhi/armbench.
