  Robots in real-world environments continuously engage with multiple users and
encounter changes that lead to unexpected conflicts in fulfilling user
requests. Recent technical advancements (e.g., large-language models (LLMs),
program synthesis) offer various methods for automatically generating repair
plans that address such conflicts. In this work, we understand how automated
repair and explanations can be designed to improve user experience with robot
failures through two user studies. In our first, online study ($n=162$), users
expressed increased trust, satisfaction, and utility with the robot performing
automated repair and explanations. However, we also identified risk factors --
safety, privacy, and complexity -- that require adaptive repair strategies. The
second, in-person study ($n=24$) elucidated distinct repair and explanation
strategies depending on the level of risk severity and type. Using a
design-based approach, we explore automated repair with explanations as a
solution for robots to handle conflicts and failures, complemented by adaptive
strategies for risk factors. Finally, we discuss the implications of
incorporating such strategies into robot designs to achieve seamless operation
among changing user needs and environments.
