MMRo: Are Multimodal LLMs Eligible as the Brain for In-Home Robotics?

TL;DR

This paper introduces the first benchmark to evaluate whether multimodal large language models can serve as the brain of in-home robots, assessing their perception, planning, reasoning, and safety capabilities.

Contribution

It presents a comprehensive benchmark with 14 metrics for evaluating MLLMs in robotic contexts and provides experimental results showing current models' limitations.

Findings

No single MLLM excels in all evaluated areas.

Current MLLMs are not yet reliable enough to be the robot's central processor.

The benchmark highlights key areas for improvement in multimodal LLMs for robotics.

Abstract

It is fundamentally challenging for robots to serve as useful assistants in human environments because this requires addressing a spectrum of sub-problems across robotics, including perception, language understanding, reasoning, and planning. The recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated their exceptional abilities in solving complex mathematical problems, mastering commonsense and abstract reasoning. This has led to the recent utilization of MLLMs as the brain in robotic systems, enabling these models to conduct high-level planning prior to triggering low-level control actions for task execution. However, it remains uncertain whether existing MLLMs are reliable in serving the brain role of robots. In this study, we introduce the first benchmark for evaluating Multimodal LLM for Robotic (MMRo) benchmark, which tests the capability of MLLMs for robot applications. Specifically, we identify four essential capabilities perception, task planning, visual reasoning, and safety measurement that MLLMs must possess to qualify as the robot's central processing unit. We have developed several scenarios for each capability, resulting in a total of 14 metrics for evaluation. We present experimental results for various MLLMs, including both commercial and open-source models, to assess the performance of existing systems. Our findings indicate that no single model excels in all areas, suggesting that current MLLMs are not yet trustworthy enough to serve as the cognitive core for robots. Our data can be found in https://mm-robobench.github.io/.