Autonomous Labs 4

Close the design–make–test–analyze loop: plan experiments, dispatch to lab robots, and analyze results autonomously.

4 curated workflows. Each fans out to several tested Paramus tools.

• Active Learning Loop

  Run a closed-loop active-learning optimization that, given results from completed experiments, updates a surrogate model, proposes the next most informative experiments via an acquisition strategy, and decides whether to continue or stop — orchestrating Paramus ML/optimization tools.When to use: a user wants the system to choose the next best experiments automatically.Multi-tool WORKFLOW with acquisition-strategy judgment.

• Experiment Campaign Planner

  Plan an autonomous experimental campaign by translating a research goal and constraints into a concrete experiment set, selecting a design strategy, and defining the design–make–test–analyze (DMTA) loop with clear stop criteria, orchestrating Paramus DOE and planning tools.When to use: a user wants to set up a closed-loop experimental campaign.Multi-tool WORKFLOW with experimental-strategy judgment.

• Lab Result Analysis

  Ingest raw results from completed lab runs, apply quality control, reduce and analyze the data against the campaign objective, and produce a decision-ready summary that feeds the next loop iteration — orchestrating Paramus analysis and statistics tools.When to use: a user has run experiments and needs analysis plus a recommendation.Multi-tool WORKFLOW with data-quality judgment.

• Robotic Protocol Dispatch

  Translate an experiment design into an executable robotic protocol and dispatch it to lab automation via Paramus OPERATE, validating the protocol against instrument capabilities and safety constraints before submission and tracking execution.When to use: a user wants to run a planned experiment set on lab robots.Multi-tool WORKFLOW with automation-safety judgment.