组合式机器的自主设计

摘要

复杂机器的设计既是人类智慧的标志，也是工程实践的基石。鉴于大型语言模型（LLMs）近期的进展，我们探讨它们是否也能学会创造。我们通过组合式机器设计的视角来审视这一问题：这一任务要求将标准化部件组装成机器，以满足在模拟物理环境中运动或操作等功能需求。为支持这一研究，我们引入了BesiegeField，一个基于机器建造游戏Besiege的测试平台，它支持基于部件的构建、物理模拟及奖励驱动的评估。利用BesiegeField，我们对具备代理工作流程的顶尖LLMs进行了基准测试，并识别出成功所需的关键能力，包括空间推理、策略性组装及指令遵循。鉴于当前开源模型的不足，我们探索了强化学习（RL）作为改进途径：我们整理了一个冷启动数据集，进行了RL微调实验，并指出了语言、机器设计与物理推理交叉领域的开放挑战。

English

The design of complex machines stands as both a marker of human intelligence and a foundation of engineering practice. Given recent advances in large language models (LLMs), we ask whether they, too, can learn to create. We approach this question through the lens of compositional machine design: a task in which machines are assembled from standardized components to meet functional demands like locomotion or manipulation in a simulated physical environment. To support this investigation, we introduce BesiegeField, a testbed built on the machine-building game Besiege, which enables part-based construction, physical simulation and reward-driven evaluation. Using BesiegeField, we benchmark state-of-the-art LLMs with agentic workflows and identify key capabilities required for success, including spatial reasoning, strategic assembly, and instruction-following. As current open-source models fall short, we explore reinforcement learning (RL) as a path to improvement: we curate a cold-start dataset, conduct RL finetuning experiments, and highlight open challenges at the intersection of language, machine design, and physical reasoning.

组合式机器的自主设计

Agentic Design of Compositional Machines

摘要

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