PRISM: Protocol Refinement through Intelligent Simulation Modeling

PRISM is a research framework for automated experimental protocol generation, validation, and execution in robotic laboratories. It integrates language-model–based reasoning, simulation-driven validation, and robot-aware execution to enable end-to-end automation without human intervention between experimental steps.

This repository accompanies the PRISM paper and provides the prompts and code used for protocol planning, protocol generation, simulation-based validation, and robotic execution.

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Overview

Figure: Overview of the PRISM framework for protocol generation and execution.
The system consists of three main stages: Protocol Planning, where user intent is converted into structured steps; Protocol Generation, where structured English instructions are transformed into robot-aware actions and iteratively refined through validation cycles in Omniverse before execution; and Real-World Execution, where the full pipeline is validated using the Luna qPCR protocol in our autonomous laboratory.

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PRISM Workflow

PRISM operates as a closed-loop system with three core stages:

1. Protocol Planning

User intent is converted into structured natural-language experimental steps using language-model–based reasoning.
This stage may involve:

• Automatically retrieving reference procedures from web-based sources
• Generating structured experimental steps (e.g., liquid handling, timing, dependencies)
• Identifying required reagents, instruments, and constraints

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2. Protocol Generation & Validation

Structured protocol descriptions are transformed into robot-aware, executable protocols.
This stage includes:

• Translation into the Argonne MADSci protocol format
• Coordination across multiple robotic instruments
• Simulation-based validation in a digital twin environment built in NVIDIA Omniverse
• Iterative refinement cycles where detected physical or sequencing errors are reported back and corrected

Protocols must pass simulation-based validation before execution.

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3. Real-World Execution

Validated protocols are executed on an autonomous laboratory platform composed of off-the-shelf robotic instruments, including:

• Opentrons OT-2 liquid handler
• PF400 robotic arm
• Azenta plate sealer and peeler

The full pipeline is demonstrated using Luna qPCR amplification.

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Benchmarking & Evaluation

PRISM supports systematic benchmarking across:

• Single-agent vs multi-agent protocol generation
• Constrained vs open-ended prompting paradigms
• Protocol correctness, ordering, and refinement efficiency

Simulation-based validation enables consistent detection of physical infeasibility prior to real-world execution.

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Repository Structure

PRISM/
├── run_prism.py                  # End-to-end pipeline (Stage 1 → Stage 2)
├── ProtocolPlanner/              # Stage 1: Protocol Planning
│   ├── run_stage1.py             # Multi-agent / single-agent LLM pipeline
│   ├── requirements.txt          # Python dependencies (openai, anthropic, google-generativeai)
│   └── Prompts/                  # Prompt templates per experiment and paradigm
│       ├── PCR/                  #   PCR: constrained, open-ended, single-agent variants
│       └── CellPainting/         #   Cell Painting: multi-agent and single-agent
├── ProtocolGenerator/Code/       # Stage 2: Protocol Generation + Simulation Validation
│   ├── run_agent.sh              # Launches Claude Code for autonomous protocol generation
│   ├── projects/prism/           # PCR project: prompts, workflow configs, simulation launcher
│   ├── slcore/                   # Simulation core (robot servers, REST gateway, motion)
│   └── assets/                   # 3D robot models and labware (USD format)
└── outputs/                      # End-to-end pipeline outputs (auto-generated)

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Prerequisites

Stage 1 (Protocol Planning):

• Python 3.10+
• API key(s) in ProtocolPlanner/.env: OPENAI_API_KEY, ANTHROPIC_API_KEY, and/or GOOGLE_API_KEY

pip install -r ProtocolPlanner/requirements.txt

Stage 2 (Protocol Generation + Simulation):

• Linux with NVIDIA GPU (Isaac Sim 5.1)
• Docker and Docker Compose (for MADSci services)
• Claude Code CLI (claude)
• See ProtocolGenerator/Code/README.md for full setup

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Quick Start

End-to-end pipeline

# Full pipeline: Stage 1 (LLM planning) → Stage 2 (code generation + simulation)
python run_prism.py --experiment pcr --paradigm constrained --model gpt-5

# With Claude Opus, open-ended paradigm
python run_prism.py --experiment pcr --paradigm open-ended --model claude-opus

Stage 1 only (no GPU required)

# Run protocol planning, skip simulation
python run_prism.py --experiment pcr --paradigm constrained --model claude-opus --stage1-only

# Or call Stage 1 directly
python ProtocolPlanner/run_stage1.py --experiment pcr --paradigm constrained --model gpt-5

# List all available configurations
python ProtocolPlanner/run_stage1.py --list

Stage 2 only (reuse existing Stage 1 output)

# Point Stage 2 at a previously generated protocol
python run_prism.py --experiment pcr \
    --stage2-only ProtocolPlanner/outputs/pcr_constrained_gpt-5_20260323_120000/final_protocol.txt

Supported models

Name	Provider	Model ID
gpt-5	OpenAI	gpt-5
gpt-4o	OpenAI	gpt-4o
claude-opus	Anthropic	claude-opus-4-6
claude-sonnet	Anthropic	claude-sonnet-4-6
gemini-pro	Google	gemini-2.5-pro
gemini-flash	Google	gemini-2.5-flash
gemini-flash-lite	Google	gemini-2.5-flash-lite

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Benchmarking & Evaluation

PRISM supports systematic benchmarking across:

• Single-agent vs multi-agent protocol generation
• Constrained vs open-ended prompting paradigms
• Protocol correctness, ordering, and refinement efficiency

Simulation-based validation enables consistent detection of physical infeasibility prior to real-world execution.

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Scope & Disclaimer

This repository is intended for research and benchmarking purposes. Protocols generated by PRISM should be independently reviewed and validated before use in safety-critical or production laboratory environments.

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Citation

If you use PRISM or build upon this work, please cite:

@article{prism2025,
  title={PRISM: Protocol Refinement through Intelligent Simulation Modeling},
  author={...},
  journal={...},
  year={2025}
}