Ron Petrick's Webpages

Workshop talk

What Would You Like to Drink? Knowledge-Level Planning for a Social Robot Bartender, R. Petrick, slides from a talk presented at the Workshop on Planning, Logic, and Social Intelligence, Technical University of Denmark, Copenhagen, Denmark, 2014-04-04.

[ slides ]


A robot coexisting with humans must not only be able to perform physical tasks, but must also be able to interact with humans in a socially appropriate manner. In many settings, this involves the use of social signals like gaze, facial expression, and language. In this talk, I describe an application of knowledge-level planning to the problem of task-based social interaction, using a robot that must interact with multiple human agents in a simple bartending domain. States are inferred from low-level sensors, using vision and speech as input modalities. High-level actions are selected by the PKS planner, which constructs plans with task, dialogue, and social actions, and provides an alternative to current mainstream methods of interaction management. In particular, PKS treats the action selection problem as an instance of planning with incomplete information and sensing, and builds plans by reasoning about how the robot's knowledge changes due to action. Examples are provided from a series of drink ordering scenarios which have been tested on a real robot with human users.