论文标题
语言条件的强化学习通过行动更正解决误解
Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action Corrections
论文作者
论文摘要
人与人之间的谈话不仅是在说话和倾听。这是一个增量过程，参与者不断建立共同的理解来排除误解。当前的智能机器人理解方法不考虑这一点。考虑到不理解，存在许多方法，但它们忽略了解决误解的增量过程。在本文中，我们提出了基于强化学习的机器人指令遵循的增量动作修复的首次形式化和实验验证。为了评估我们的方法，我们提出了一系列基准环境，以在语言条件的强化学习中进行操作校正，并利用合成教师生成语言目标及其相应的校正。我们表明，强化学习者可以成功地学习了解对误解的说明的增量更正。
Human-to-human conversation is not just talking and listening. It is an incremental process where participants continually establish a common understanding to rule out misunderstandings. Current language understanding methods for intelligent robots do not consider this. There exist numerous approaches considering non-understandings, but they ignore the incremental process of resolving misunderstandings. In this article, we present a first formalization and experimental validation of incremental action-repair for robotic instruction-following based on reinforcement learning. To evaluate our approach, we propose a collection of benchmark environments for action correction in language-conditioned reinforcement learning, utilizing a synthetic instructor to generate language goals and their corresponding corrections. We show that a reinforcement learning agent can successfully learn to understand incremental corrections of misunderstood instructions.
