论文标题
公理学习和信念追踪机器人技术透明决策
Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics
论文作者
论文摘要
机器人对其决策和信念的描述的能力促进了与人类的有效合作。在包括基于知识的推理方法和数据驱动的学习算法的集成机器人系统中，提供这种透明度尤其具有挑战性。为了应对这一挑战，我们的建筑融合了非单调逻辑推理，深度学习和决策树的互补优势。在推理和学习期间，该体系结构使机器人能够对其决策，信念和假设行动的结果提供按需关系描述。这些功能是在场景的背景下进行接地和评估的，理解了使用模拟的图像和图像操纵桌面对象执行的任务和计划任务。
A robot's ability to provide descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing such transparency is particularly challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning algorithms. Towards addressing this challenge, our architecture couples the complementary strengths of non-monotonic logical reasoning, deep learning, and decision-tree induction. During reasoning and learning, the architecture enables a robot to provide on-demand relational descriptions of its decisions, beliefs, and the outcomes of hypothetical actions. These capabilities are grounded and evaluated in the context of scene understanding tasks and planning tasks performed using simulated images and images from a physical robot manipulating tabletop objects.
