面向光流组件的智能语言到目标合成
Agentic Language-to-Objective Synthesis for Optofluidic Assembly
AI总结：
提出Speak-to-Objective模块化智能流水线，利用条件大语言模型将口语或书面指令转换为可微目标函数，实现光流控微粒子组装，并支持用户反馈学习。
摘要：
基于光的先进制造日益需要可编程、闭环工具，将人类设计意图转化为小尺度上的可执行操作。然而，在机器人和制造模式中仍存在一个关键瓶颈：将用户意图转化为机器可读且可靠执行的目标。尽管微机器人通过光驱动流体提供了多功能操控，但数学上可处理的目标规范仍然手动且难以重用。本文介绍Speak-to-Objective，一个模块化智能流水线，使用条件大语言模型将口语或书面指令转换为完全可微的目标函数，用于在约束感知逆求解器（SLSQP）和实验光流控平台上组装微粒。该方法采用紧凑循环——感知→组合→提议→行动→报告与学习——将目标作为意图与驱动之间的接口，分离组装或图案化什么与如何驱动，同时从用户反馈中学习。流水线组合几何、间距和分配/拓扑项，生成鲁棒的描述性目标，从部分轨迹组装并在扰动后恢复，以及用于精确定位的显式目标，所有均以执行器无关的方式。使用激光诱导热粘性流作为物理驱动模式，我们展示了自然语言可编程的、基于光的微尺度粒子图案组装在微流控环境中。除了对可编程微组装的直接影响，以及使用激光诱导光流控驱动作为降复杂度实验平台，我们的工作指向自驱动、AI辅助的光学制造平台，其中自然语言、可微目标和激光驱动耦合为可重复使用的数字工作流。
英文摘要：
Light-based advanced manufacturing increasingly requires programmable, closed-loop tools that translate human design intent into executable operations at small length scales. Yet a key bottleneck persists across robotic and manufacturing modalities: turning user intent into machine-readable objectives that are reliably executable. While micro-robotics offers versatile manipulation via optical actuation of fluids, mathematically tractable goal specification remains manual and hard to reuse. Here, we introduce Speak-to-Objective, a modular agentic pipeline that uses a conditioned Large Language Model (LLM) to translate spoken or written commands into fully differentiable objective functions for assembling microparticles in a constraint-aware inverse solver (SLSQP) and on an experimental optofluidic platform. The approach employs a compact loop - perceive -> compose -> propose -> act -> report & learn - that treats the objective as the interface between intent and actuation, separating what to assemble or pattern from how to actuate, while learning from user feedback. The pipeline composes geometry, spacing, and assignment/topology terms to generate robust descriptive objectives that assemble from partial traces and recover after perturbations, as well as explicit objectives for precise placement, all in an actuator-agnostic fashion. Using laser-induced thermoviscous flows as the physical actuation modality, we demonstrate natural-language-programmable, light-based microscale assembly of particle patterns in a microfluidic environment. Beyond its immediate impact on programmable microassembly, and using laser-induced optofluidic actuation as a reduced-complexity experimental platform, our work points toward self-driving, AI-assisted optical manufacturing platforms in which natural language, differentiable objectives, and laser-based actuation are coupled into a reusable digital workflow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
