It Takes a Village to Raise a Robot: Evolving Autonomous Lawnmower Behaviors through Human Selection

As autonomous lawnmowers become more common in shared spaces, aligning their behavior with human expectations and norms is increasingly important. Existing approaches often optimize for fixed objectives, limiting adaptability to diverse contexts. This work explores an alternative by enabling users to guide autonomous behavior development without fixed objectives. A prototype system allowed participants to interact with a simulated environment, using subjective preferences and genetic algorithms to generate lawnmower behaviors across generations. The study emphasized open-ended exploration, analyzing participant interactions and semi-structured interviews through reflexive thematic analysis. Results reveal detailed and reflective accounts of lawnmower behavior. We discuss these results in the context of our design decisions and how they affected the user's journey through a complex solution space. Ultimately, this work demonstrates how interactive genetic algorithms can surface user values and interpretations, potentially serving as both a behavior design tool and novel method to generatively explore social meaning in human-robot interaction.