Frontier LLMs Enter the World of Robotics: A Cross-Model Evaluation on RoboColiseum

原文：Frontier LLMs Enter the World of Robotics: A Cross-Model Evaluation on RoboColiseum

作者：Da Huang, Jichao Wang, Junhui Wu, Tong Ran, Linting Guo, Siying Zhang, Chuang Men, Sukai Wang, Yue Zhou, Maoqing Yao, Mo Wu

来源：arXiv cs.RO（机器人）

正文

Computer Science > Robotics

arXiv:2610.27865v1 (cs)

[Submitted on 20 Oct 2026]

Title:Frontier LLMs Enter the World of Robotics: A Cross-Model Evaluation on RoboColiseum

Authors:Da Huang, Jichao Wang, Junhui Wu, Tong Ran, Linting Guo, Siying Zhang, Chuang Men, Sukai Wang, Yue Zhou, Maoqing Yao, Mo Wu

View a PDF of the paper titled Frontier LLMs Enter the World of Robotics: A Cross-Model Evaluation on RoboColiseum, by Da Huang and 10 other authors

View PDF

HTML (experimental)

Abstract:Recent studies have explored the potential of multimodal large language models for robot control, yet the models’ embodied capabilities remain insufficiently characterized across models, tasks, and execution settings. We conduct a multidimensional comparative evaluation on RoboColiseum across four tracks: Instruction Following, Spatial Reasoning, General Manipulation, and Robustness. We implement Agent2Act, a control harness that integrates GPT-6 Astra, GPT-6.1 Sol, Claude Opus 5.5, and Gemini 4 Argon into the evaluation environment through shared observation, action execution, and feedback interfaces. Under common task settings and scoring criteria, all four models exhibit a consistent capability profile: high instruction-following scores, with three approaching perfect performance; strong spatial reasoning; and general manipulation scores below those of the other three tracks. Within the evaluated configurations, Astra leads instruction following and robustness, while Opus leads spatial reasoning, general manipulation, and overall performance. These results reveal complementary strengths across models and a persistent gap between task understanding and reliable physical execution. Component comparisons on selected tasks further show that harness design affects task success, planning cost, and execution time. Additionally, case studies with Astra examine whole-body coordination, manipulation across robot embodiments, precision manipulation, dexterous-hand manipulation, and real-robot tasks, providing further insight into the model’s capabilities and limitations across embodied tasks.

Subjects:

Robotics (cs.RO)

Cite as:

arXiv:2610.27865 [cs.RO]

(or

arXiv:2610.27865v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.27865

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

主题

机器人

由「前沿雷达」于 2026-10-20 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。