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RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models

Zijun Long, George Killick, Richard McCreadie, Gerardo Aragon Camarasa

TL;DR

Robotic vision tasks such as object segmentation, identification, and defect detection are usually tackled with task-specific models, hindering integration into a unified pipeline. The authors introduce RoboLLM, a modular framework that uses a lightweight BEiT-3 Multimodal Large Language Model as a common backbone and attaches task-specific heads for each vision task, significantly reducing engineering overhead and enabling transfer across tasks. They also present a compact BEiT-3 variant and demonstrate state-of-the-art results on ARMBench across all three tasks, including a recall@1 of 97.8% for object identification and strong segmentation performance, along with robustness to varying object counts and out-of-distribution conditions. The work highlights the practical viability of MLLMs as universal backbones for complex robotic perception, offering a scalable path to more maintainable and upgradable robotic vision systems.

Abstract

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.

RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models

TL;DR

Robotic vision tasks such as object segmentation, identification, and defect detection are usually tackled with task-specific models, hindering integration into a unified pipeline. The authors introduce RoboLLM, a modular framework that uses a lightweight BEiT-3 Multimodal Large Language Model as a common backbone and attaches task-specific heads for each vision task, significantly reducing engineering overhead and enabling transfer across tasks. They also present a compact BEiT-3 variant and demonstrate state-of-the-art results on ARMBench across all three tasks, including a recall@1 of 97.8% for object identification and strong segmentation performance, along with robustness to varying object counts and out-of-distribution conditions. The work highlights the practical viability of MLLMs as universal backbones for complex robotic perception, offering a scalable path to more maintainable and upgradable robotic vision systems.

Abstract

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.
Paper Structure (27 sections, 4 figures, 3 tables)

This paper contains 27 sections, 4 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. BACKGROUND
  3. Emergence of Multimodal Large Language Models
  4. Image Segmentation
  5. Image Retrieval
  6. Defect Detection
  7. MATERIALS and METHODS
  8. Overview
  9. Backbone Encoder
  10. Object Segmentation
  11. Task-Specific Head
  12. Feature Pyramid Construction
  13. Object Detection Procedure
  14. Advantages and Rationale
  15. Object Identification
  16. ...and 12 more sections

Figures (4)

  • Figure 1: The task-specific adaptions to BEiT-3, proposed in our RoboLLM framework, for tackling each challenge in ARMBench. BEiT-3's large-scale vision-language pretraining allows it to be easily and effectively transferred to downstream tasks.
  • Figure 2: Our lightweight modification of the BEiT-3 backbone only remains the vision experts.
  • Figure 3: Numpy-like pseudo-code for the core of an implementation of our framework for Object Identification task.
  • Figure 4: Number of object instances per image against Mean Average Precision at 50 on the Mixed-Object tote test-set.