Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations
Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations
We present a system to infer and execute a human-readable program from a real-world demonstration. The system consists of a series of neural networks to perform perception, program generation, and program execution. Leveraging convolutional pose machines, the perception network reliably detects the bounding cuboids of objects in real images even …