Closed-Loop: Vision-Guided Experimental Control in Self-Driving Labs
Bibliographic record
Abstract
In experimental chemistry, actions are adjusted based on what we see—such as dosing until dissolution, heating until melting, or stirring until mixing is complete. However, current self-driving labs (SDLs) do not monitor these visual cues. HeinSight 4.0 fills this gap by integrating computer vision into SDLs to enable real-time experimental adjustments based on visual feedback. The computer vision system detects equipment (e.g., reactor, vial), classifies chemical phases (solid, liquid, air), and analyzes image features such as turbidity and color. By tracking these physical characteristics frame by frame, HeinSight 4.0 infers physical states (e.g., dissolution, separation). This data feeds into a rule-based system that integrates with the SDL to make real-time experimental adjustments (e.g., stir, heat). We demonstrate HeinSight 4.0 adaptability for iterative refinement of two pharmaceutical case studies: purification (solubility screening) and drug formulation (melt spray congeal). To support broader adoption, we integrated HeinSight 4.0 into a hardware-agnostic SDL architecture and deployed it across two institutions with distinct robotic systems. As an open-source tool, HeinSight 4.0 enables SDLs to see, think, and act in real time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".