论文标题
机器人对话框和导航任务的学习和推理
Learning and Reasoning for Robot Dialog and Navigation Tasks
论文作者
论文摘要
强化学习和概率推理算法旨在分别从互动经验中学习和与概率上下文知识的推理。在这项研究中，我们开发了用于机器人任务完成的算法，同时研究了增强学习和概率推理技术的补充优势。机器人从反复试验的经验中学习以增加其声明性知识基础，并且可以使用增强知识来加速潜在的不同任务中的学习过程。我们使用执行对话框和导航任务的移动机器人实施并评估了开发的算法。从结果来看，我们可以通过人类知识的推理和从任务完成经验学习来提高机器人的性能。更有趣的是，机器人能够从导航任务中学习以改善其对话策略。
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.
