Home robots performing personalized tasks must adeptly balance user preferences with environmental affordances. We focus on organization tasks within constrained spaces, such as arranging items into a refrigerator, where preferences for placement collide with physical limitations. The robot must infer user preferences based on a small set of demonstrations, which is easier for users to provide than extensively defining all their requirements. While recent works use Large Language Models (LLMs) to learn preferences from user demonstrations, they encounter two fundamental challenges. First, there is inherent ambiguity in interpreting user actions, as multiple preferences can often explain a single observed behavior. Second, not all user preferences are practically feasible due to geometric constraints in the environment. To address these challenges, we introduce APRICOT, a novel approach that merges LLM-based Bayesian active preference learning with constraint-aware task planning. APRICOT refines its generated preferences by actively querying the user and dynamically adapts its plan to respect environmental constraints. We evaluate APRICOT on a dataset of diverse organization tasks and demonstrate its effectiveness in real-world scenarios, showing significant improvements in both preference satisfaction and plan feasibility. The project website is at https://portal-cornell.github.io/apricot/

Researchers from the University of Waterloo got a valuable assist from artificial intelligence (AI) tools to help capture and analyze data from professional hockey games more quickly and more accurately, something which could have implications for the business of sports. The growing field of hockey analytics currently relies on the manual analysis of video footage from games. Professional hockey teams across the sport, notably in the National Hockey League (NHL), make important decisions regarding players' careers based on that information. "The goal of our research is to interpret a hockey game through video more effectively and efficiently than a human," said Dr David Clausi, a professor in Waterloo's Department of Systems Design Engineering. Bounding boxes are used to identify players as they move on the ice in broadcast game video.