Agentic Language-to-Objective Synthesis for Optofluidic Assembly
Researchers introduce Speak-to-Objective, a modular agentic pipeline that uses a conditioned LLM to translate spoken or written commands into fully differentiable objective functions for assembling microparticles in a constraint-aware inverse solver and on an experimental optofluidic platform. The approach separates what to assemble from how to actuate, learns from user feedback, and demonstrates natural-language-programmable microscale assembly using laser-induced thermoviscous flows.
Article intelligence
Key points
- Speak-to-Objective pipeline translates natural language into differentiable objective functions for microparticle assembly.
- It uses a perceive->compose->propose->act->report&learn loop, treating the objective as the interface between intent and actuation.
- Demonstrated on an optofluidic platform with laser-induced thermoviscous flows.
- Points toward self-driving AI-assisted optical manufacturing platforms.
Why it matters
This matters because speak-to-Objective pipeline translates natural language into differentiable objective functions for microparticle assembly.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27643] Agentic Language-to-Objective Synthesis for Optofluidic Assembly
[Submitted on 26 May 2026]
Title:Agentic Language-to-Objective Synthesis for Optofluidic Assembly
View a PDF of the paper titled Agentic Language-to-Objective Synthesis for Optofluidic Assembly, by Ivan Saraev and 6 other authors
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Abstract: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.
Comments: 21 pages, 5 figures
Subjects:
Robotics (cs.RO); Optics (physics.optics)
Cite as: arXiv:2605.27643 [cs.RO]
(or arXiv:2605.27643v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.27643
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Moritz Kreysing [view email] [v1] Tue, 26 May 2026 20:03:54 UTC (1,836 KB)
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