Best Practices Overview¶
Essential best practices for successful robot learning projects.
Data Best Practices¶
Quality over quantity - Collect high-quality demonstrations - Validate data before training - Maintain diverse initial conditions
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Training Best Practices¶
Systematic approach - Start simple, add complexity gradually - Monitor metrics closely - Use version control for experiments
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Deployment Best Practices¶
Safety first - Test thoroughly in simulation - Gradual rollout to real hardware - Monitor deployed systems
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Quick Reference¶
Data Collection¶
✓ DO: Diverse demonstrations, quality checks, multi-modal recording
✗DON'T: Accept failures, skip validation, ignore distribution
Training¶
✓ DO: Track experiments, use checkpoints, validate regularly
✗DON'T: Skip validation, ignore overfitting, train without baselines
Deployment¶
✓ DO: Test in sim first, optimize for target hardware, monitor performance
✗DON'T: Deploy untested, skip optimization, ignore edge cases