Claude/ai agent prompt guidelines 5 fw h6 #8663
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Added the official research proposal (v3) for developing medical motion content generation and augmentation technology for Physical AI training. This document outlines the 4-year project plan using NVIDIA ecosystem (MONAI, Isaac Sim, ORBIT-Surgical, Isaac Lab).
Created comprehensive project structure for medical Physical AI research: Project Structure: - docs/: Research proposal and technical documentation - data/: Raw, processed, and synthetic data directories - src/: Source code organized by functionality - data/: Data processing and loading - generation/: MONAI, MAISI, Cosmos models - simulation/: ORBIT-Surgical, Isaac Sim integration - training/: Isaac Lab, LeRobot frameworks - evaluation/: Metrics (FVD, RMSE) and validation - notebooks/: Jupyter notebooks for experimentation - scripts/: Utility scripts including dataset downloaders - configs/: Configuration files - experiments/: Experiment logs and results Key Additions: 1. Dataset documentation (JIGSAWS, Cholec80, EndoVis) 2. Download script for surgical video datasets 3. Requirements.txt with full dependency stack 4. PoC notebook for MONAI Generative Models testing Technical Stack: - MONAI Generative Models for video generation - MAISI for CT synthesis - ORBIT-Surgical for surgical robot simulation - Isaac Lab + LeRobot for robot learning Next Steps: - Download JIGSAWS dataset - Run PoC notebook to validate video generation - Setup ORBIT-Surgical environment
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Caution Review failedThe pull request is closed. WalkthroughThis PR introduces foundational project infrastructure for Physical AI Research. It adds a README outlining project overview and objectives, a technical document cataloging open-source surgical video datasets, a Jupyter notebook demonstrating MONAI generative model workflows (2D/3D diffusion pipelines), a complete Python requirements specification, and a command-line utility script for downloading surgical datasets (JIGSAWS, Cholec80) with structured directory scaffolding and metadata generation. Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes
✨ Finishing touches
🧪 Generate unit tests (beta)
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