FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks

2024-08-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Manufacturing & Industrial · Depth: Expert, extended

Summary

Fujitsu Limited and Carnegie Mellon University researchers have introduced FieldWorkArena, a new benchmark designed to evaluate agentic AI in real-world field work environments, specifically manufacturing and logistics. This benchmark addresses the limitations of existing web-based evaluations by incorporating complex, multi-stage tasks and multimodal inputs like on-site videos, images, and factory documents. FieldWorkArena defines a new action space for agentic AI, encompassing planning, perception, and action functions, and improves evaluation metrics to assess performance in ambiguous, diverse tasks. The dataset, comprising over 40 data types and approximately 400 field-specific queries from actual factory and warehouse scenes, is publicly available on HuggingFace, with the evaluation program on GitHub. Initial evaluations using MLLMs like GPT-4o, Gemini 2.0 Flash, and Claude 3.7 Sonnet demonstrated the benchmark's feasibility and highlighted current MLLM limitations in complex field tasks.

Key takeaway

For research scientists developing agentic AI for industrial applications, FieldWorkArena offers a critical tool to validate model performance in realistic, complex environments. You should utilize this benchmark to identify specific strengths and weaknesses of your MLLMs in handling multimodal inputs and multi-stage tasks, guiding future development towards robust, field-ready AI agents. Focus on improving agent planning, perception, and action capabilities to address the identified limitations in current MLLMs.

Key insights

FieldWorkArena provides a real-world benchmark for agentic AI, moving beyond web-based evaluations to complex, multimodal field tasks.

Principles

• Agentic AI requires multimodal input for real-world tasks.
• Evaluation must account for ambiguity and continuous values.
• Complex tasks demand planning, perception, and action capabilities.

Method

FieldWorkArena defines an action space for agentic AI, categorizes tasks into Planning, Perception, and Action, and uses a refined evaluation method with Correctness and Numerical scores for granular performance assessment.

In practice

• Use FieldWorkArena to benchmark agentic AI for industrial applications.
• Train MLLMs on multimodal data for field work support.
• Develop agents capable of sequential reasoning and tool selection.

Topics

• Agentic AI Benchmarking
• Field Work Automation
• Multimodal LLMs
• Action Space Definition
• Real-World Datasets

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.