Cosmos-Ready Digital Twin Evaluation Pipeline

A production-style digital twin evaluation pipeline designed for Cosmos-era physical AI systems.

This project implements a production-style digital twin evaluation pipeline using Isaac Sim-style data capture and a Cosmos-ready AI integration architecture.

It demonstrates how simulation data can be structured, evaluated, and transformed into decision-ready outputs under varying real-world conditions.

The goal is NOT to fake Cosmos access.

The goal is to prove:

• I understand the actual pipeline needed for COSMOS Predict and Reason
• I can build it correctly
• I can create a structure this is ready to plug into Cosmos the moment it becomes available

• End-to-end digital twin evaluation pipeline design
• Simulation → AI → decision workflow integration
• Scenario-based perception robustness testing
• Production-style data structuring and artifact management
• Cosmos-ready architecture without relying on unavailable APIs

run_0004_predict_blur         | conf: 0.61 | risk: medium | REVIEW | send_to_review_queue
run_0004_predict_dim_lighting | conf: 0.71 | risk: medium | REVIEW | send_to_review_queue
run_0004_predict_noise        | conf: 0.61 | risk: medium | REVIEW | send_to_review_queue
run_0004_predict_occlusion    | conf: 0.61 | risk: medium | REVIEW | send_to_review_queue

• baseline run remains strongest
• degraded conditions reduce confidence
• all degraded runs are flagged for review
• no unsafe automatic decisions are made

This mirrors how real perception validation pipelines behave in production systems.

In real physical AI systems, the challenge is NOT just generating outputs.

It is:

• ingesting simulator data correctly
• preserving telemetry context
• evaluating perception robustness
• structuring AI requests and responses
• making downstream decisions

This project solves that pipeline problem.

Isaac Sim-style capture
→ import_isaac_capture.py
→ structured run bundle

→ build_cosmos_request.py
→ parse_cosmos_response.py

→ mock mode (current)
→ real Cosmos mode (future-ready scaffold)

→ decision artifact
→ evaluation report
→ comparison across runs

baseline run
→ simulate_predict_output.py
→ transformed scenario runs

→ request / reasoning / decision pipeline
→ comparison output

• Isaac-style frame import
• telemetry ingestion
• structured run packaging
• Cosmos request generation
• response normalization
• decision logic
• comparison reporting
• endpoint scaffolding

• Cosmos Predict outputs (no public API yet)
• Cosmos Reason executed in mock mode

The architecture is REAL.
Only the model call is simulated.

• run_0004
• imported Isaac-style frames and telemetry
• used as evaluation baseline

• run_0004_predict_blur
• run_0004_predict_dim_lighting
• run_0004_predict_noise
• run_0004_predict_occlusion

These runs simulate degraded perception conditions that would normally be handled by Cosmos Predict.

Each run produces structured outputs:

• frames/
• video/video.mp4
• telemetry/telemetry.json
• manifests/run_manifest.json
• requests/cosmos_request.json
• responses/cosmos_response.json
• decisions/decision.json
• reports/evaluation_report.md

• Python
• OpenUSD (Universal Scene Description)
• NVIDIA Isaac Sim

• JSON-based structured data (telemetry, manifests, requests, responses)
• Scenario-based evaluation pipelines
• File-based run artifact system

• NVIDIA Cosmos (integration-ready architecture)
• Simulated Cosmos Predict outputs (no public API access)
• Mock Cosmos Reason execution layer

• Perception robustness evaluation
• Scenario-based testing (blur, noise, lighting, occlusion)
• Confidence and risk scoring
• Decision routing (review vs automated action)
• Production-style pipeline architecture

This system is designed to separate simulation, inference, and evaluation layers — allowing real AI models (such as Cosmos Predict and Reason) to be integrated without changing the surrounding pipeline.

Dartayous Hunter - Digital Twin Engineer (NVIDIA-focused)