Proactive Environment Design to Enhance Reward Alignment and Human Intent Prediction

Fluent human-robot interaction requires robots to anticipate human intent, understanding the goals, motion, and reward functions that drive their behavior. While prior work focuses on algorithms for reward alignment and intent inference, it often overlooks a critical factor shaping interaction: the environment. The physical and task environment governs how humans and robots perceive, act, and coordinate, yet it is typically treated as fixed rather than as something that can be designed. My research develops novel techniques for environment design (ED), optimizing the layout and structure of shared spaces to support reward alignment and enhance legibility. I formulate ED as an optimization over parameters that influence human and robot behavior, using Quality Diversity algorithms and Bayesian Optimization to identify informative and high performing environments. Building on preliminary results in tabletop collaboration and navigation tasks, I aim to close the loop between ED and human understanding---using ED not only to help robots better interpret humans, but also to help humans better interpret robot policies and intentions. Future directions will also explore how ED can be applied to improve key human factors such as trust, comfort, and interpretability in human-robot collaboration. Ultimately, I envision robots that not only learn from humans but also design their environments, actively shaping the world around them to foster trust, transparency, and fluent interaction.