Abstract
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.
Abstract (translated)
本论文研究了构建灵活且通用的机器人的一种模型学习和在线规划方法。具体来说,我们研究如何利用底层环境转换模型中局部性和稀疏性结构,以提高模型泛化能力、数据效率和运行时效率。我们提出了一种新领域定义语言,称为PDSketch。它允许用户 flexibly 定义转换模型中的高级别结构,例如对象和特征依赖关系,类似于程序员使用TensorFlow或PyTorch来指定卷积神经网络的内核大小和隐藏维度。转换模型的细节将由可训练的神经网络填充。基于定义的结构和学习参数,PDSketch自动生成领域无关的计划启发式,而无需额外的训练。这些推导启发式加速了针对新目标的性能时间规划。
URL
https://arxiv.org/abs/2303.05501
