言語モデルによるソフトモジュラーロボット設計の探索
On the Exploration of LM-Based Soft Modular Robot Design
大規模言語モデルを用いて、ユーザ指示と物理法則を考慮しながらソフトモジュラーロボットの構造を生成し、シミュレーションによるフィードバックで反復改善する設計手法を提案した。
著者: Weicheng Ma, Luyang Zhao, Chun-Yi She, Yitao Jiang, Alan Sun, Bo Zhu, Devin Balkcom, Soroush Vosoughi
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Recent large language models (LLMs) have demonstrated promising capabilities in modeling real-world knowledge and enhancing knowledge-based generation tasks. In this paper, we further explore the potential of using LLMs to aid in the design of soft modular robots, taking into account both user instructions and physical laws, to reduce the reliance on extensive trial-and-error experiments typically needed to achieve robot designs that meet specific structural or task requirements. Specifically, we formulate the robot design process as a sequence generation task and find that LLMs are able to capture key requirements expressed in natural language and reflect them in the construction sequences of robots. To simplify, rather than conducting real-world experiments to assess design quality, we utilize a simulation tool to provide feedback to the generative model, allowing for iterative improvements without requiring extensive human annotations. Furthermore, we introduce five evaluation metrics to assess the quality of robot designs from multiple angles including task completion and adherence to instructions, supporting an automatic evaluation process. Our model performs well in evaluations for designing soft modular robots with uni- and bi-directional locomotion and stair-descending capabilities, highlighting the potential of using natural language and LLMs for robot design. However, we also observe certain limitations that suggest areas for further improvement.
