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

Bibliographic Data

ID22190669
AuthorsRyotaro Muramatsu (0009-0009-7624-7628, Kyoto University), Dražen Brščić (0000-0001-8477-6460, Kyoto University), Takayuki Kanda (0000-0002-9546-5825, Kyoto University)
Year2026
Volume15
Issue2
Pages1-21
Publication date2026-03-31
Open AccessNo
TypeARTICLE
VenueACM Transactions on Human-Robot Interaction (JOURNAL)
Journal identifiersISSN: 2573-9522 • E-ISSN: 2573-9522
PublisherAssociation for Computing Machinery (ACM) (PUBLISHER)
DOI10.1145/3772064
OpenAlexW4415312897
LanguageEN
References cited45

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

GRASP · Negotiation · Reinforcement learning · Robot · Social relation · Task Analysis · Reinforcement Learning in Robotics · Robot Manipulation and Learning · Social Robot Interaction and HRI

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Open DOIOpen Access