  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.
