arXiv 2005.098332020-05cs.AI

机器人对话和导航任务的学习和推理

Learning and Reasoning for Robot Dialog and Navigation Tasks

USTC（中国科学技术大学）；SUNY Binghamton（纽约州立大学宾汉姆顿分校）；UT Austin（德克萨斯大学奥斯汀分校）

Keting Lu, Shiqi Zhang, Peter Stone, Xiaoping Chen

备注：: arXiv admin note: substantial text overlap with arXiv:1809.11074

摘要：

强化学习和概率推理算法分别针对交互经验和概率上下文知识进行学习和推理。在这项研究中，我们开发了机器人任务完成算法，同时寻找强化学习和概率推理技术的互补优势。机器人从试错经验中学习，以增强其声明性知识库，并且增强的知识可用于加速潜在不同任务的学习过程。我们已经实施和评估的算法，使用移动机器人进行对话和导航任务。从结果中，我们看到，我们的机器人的性能可以通过与人类知识的推理和学习任务完成的经验来提高。更有趣的是，机器人能够从导航任务中学习，以改进其对话策略。

英文摘要：

Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In this research, we develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base, and the augmented knowledge can be used for speeding up the learning process in potentially different tasks. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks. From the results, we see that our robot's performance can be improved by both reasoning with human knowledge and learning from task-completion experience. More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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