  This paper studies a model learning and online planning approach towards
building flexible and general robots. Specifically, we investigate how to
exploit the locality and sparsity structures in the underlying environmental
transition model to improve model generalization, data-efficiency, and
runtime-efficiency. We present a new domain definition language, named
PDSketch. It allows users to flexibly define high-level structures in the
transition models, such as object and feature dependencies, in a way similar to
how programmers use TensorFlow or PyTorch to specify kernel sizes and hidden
dimensions of a convolutional neural network. The details of the transition
model will be filled in by trainable neural networks. Based on the defined
structures and learned parameters, PDSketch automatically generates
domain-independent planning heuristics without additional training. The derived
heuristics accelerate the performance-time planning for novel goals.
