How Can Robots Acquire Venue-Specific Social Strategies? The Case of Robot Clerks Learning Sales Negotiations

Social robots are gradually being integrated into diverse social spheres, assuming multifaceted roles such as store clerks. Many of the interactive tasks entrusted to these robots are intricate, requiring more than simply responding to requests, and thus are not easily preprogrammed. One such task is sales negotiation. Empowering robots with negotiation skills is challenging due to the nuanced, context-dependent nature of effective tactics, which typically vary across different stores. To tackle this, we explore the potential of reinforcement learning without demonstrations for robots to grasp complex social interactions and adapt to specific environments, using sales negotiation as a test case. Drawing insights from interviews with experienced sales clerks and observations of negotiation role-plays, we identified essential verbal and nonverbal features. Coupled with a fast-learning reinforcement method, we evaluated the system through interactions with human participants in two distinct store settings. Results show that the robot was able to acquire negotiation strategies tailored to each environment.