Resolve Issue #204: Complete TFRecord Generation Solution for Learning to Simulate

[nsrawat0333]

🔬 Resolves Issue #204: TFRecord Generation for Learning to Simulate

This PR provides a comprehensive solution to the long-standing Issue #204, addressing the 3+ year conversation thread about generating train.tfrecord files for custom physics simulations.

❓ Original Questions Resolved:

1. @cwl1999: "Can you provide the generated data train.tfrecord Source dataset file? When I forcibly open it, I can only see the garbled code."

   • ✅ SOLVED with complete generation pipeline and human-readable reader

2. @yours612: "How are vel_mean, vel_std, acc_mean, and acc_std calculated in metadata?"

   • ✅ SOLVED with detailed implementation matching paper methodology

3. @Social-Mean: "How can I create such a test.tfrecord file?"

   • ✅ SOLVED with complete workflow and cloth simulation examples

4. @yq60523: Multiple questions about step_context, statistics computation, and error accumulation

   • ✅ SOLVED with comprehensive documentation addressing all aspects

📦 Solution Components:

1. generate_tfrecord_dataset.py (500+ lines)

• Complete TFRecord generation from simulation data
• Automatic statistics calculation (vel_mean, vel_std, acc_mean, acc_std)
• Sample cloth dataset creation for testing
• Support for step_context (global features)
• Proper binary encoding matching Learning to Simulate format

2. tfrecord_reader_example.py (300+ lines)

• Human-readable TFRecord content inspection
• Solves the "garbled code" issue (TFRecords are binary format)
• Raw parsing demonstration and debugging tools
• Statistics verification against metadata

3. TFRECORD_GENERATION_GUIDE.md (2,000+ words)

• Comprehensive documentation explaining TFRecord format
• Step-by-step workflow from simulation data to trained model
• Statistics calculation methodology with code examples
• Troubleshooting guide for common issues

4. requirements-tfrecord.txt + ISSUE_204_SOLUTION.md

• Dependencies specification
• Complete solution summary for GitHub issue

🔬 Key Technical Contributions:

TFRecord Format Explanation:

tf.train.SequenceExample {
    context: {                    # Static per trajectory
        'key': trajectory_id,
        'particle_type': bytes   # [N_particles] 
    },
    feature_lists: {             # Time-varying
        'position': [bytes, ...], # [time_steps][N_particles, dims]
        'step_context': [bytes, ...] # [time_steps][context_dims]
    }
}

[polarbe]

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