论文标题
使用深度学习来引导层次机器人计划的抽象
Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning
论文作者
论文摘要
本文解决了学习抽象的问题，这些问题可以提高机器人计划性能，同时提供可靠性的坚定保证。尽管最先进的层次机器人计划算法允许机器人有效地计算长途运动计划，以实现用户所需的任务，但这些方法通常依赖于需要由专家手动设计的环境依赖性状态和动作抽象。 我们提出了一种新方法来引导整个层次规划过程。这使我们能够使用具有自动生成的机器人特定体系结构的深神经网络预测的关键区域来自动计算新环境的抽象状态和动作。我们表明，学习的抽象可以与新型的多源双向层次机器人计划算法一起使用，该算法是声音且概率完成的。对使用人类和非全面机器人对二十种不同环境进行的广泛经验评估表明，（a）我们学识渊博的抽象提供了有效的多源层次层次计划所需的信息； （b）这种学习，抽象和计划的方法在培训期间未见的测试环境上的计划时间方面，超过最先进的基准的胜于最先进的基线。
This paper addresses the problem of learning abstractions that boost robot planning performance while providing strong guarantees of reliability. Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. We present a new approach for bootstrapping the entire hierarchical planning process. This allows us to compute abstract states and actions for new environments automatically using the critical regions predicted by a deep neural network with an auto-generated robot-specific architecture. We show that the learned abstractions can be used with a novel multi-source bi-directional hierarchical robot planning algorithm that is sound and probabilistically complete. An extensive empirical evaluation on twenty different settings using holonomic and non-holonomic robots shows that (a) our learned abstractions provide the information necessary for efficient multi-source hierarchical planning; and that (b) this approach of learning, abstractions, and planning outperforms state-of-the-art baselines by nearly a factor of ten in terms of planning time on test environments not seen during training.
