RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models

• URL: http://arxiv.org/abs/2310.10221v2
• Date: Fri, 23 Feb 2024 15:18:31 GMT
• Title: RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models
• Authors: Zijun Long and George Killick and Richard McCreadie and Gerardo Aragon Camarasa
• Abstract summary: Multimodal Large Language Models (MLLMs) have emerged as novel backbones for various downstream tasks. We introduce the RoboLLM framework, equipped with a BEiT-3 backbone, to address all visual perception tasks in the ARMBench challenge.
• Score: 4.4173427917548524
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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