Human-robot interaction

Robots interact with people in ever more profound ways as they are being used in domestic and public environments. These environments are typically unknown, dynamically changing and populated by people. In the civil domain robots appear as (museum) tour guides, or travel agents. In health care robots support independent living or assist care personnel. In all these applications it is tacitly assumed that robots possess the cognitive intelligence to perform these tasks, but this is often far from being true. To interact with an individual a robot needs to approach a person, attract and monitor attention, and possess context awareness. In multi-party settings turn taking and joint attention are fundamental cognitive skills for a robot. For natural HRI it would seem useful if a robot could understand and provide social cues like co-speech gestures, and facial expressions. Should a robot be persuasive, entertaining or submissive? Do robots need a theory of mind?

The course Human-Robot Interaction addresses some of the fundamental problems of interacting with humans. It combines knowledge and experience from cognitive sciences, artificial intelligence and robotics. The course starts with explaining how a probabilistic framework can be used to incorporate context information from noisy sensors in the robot’s world model. The next step is to make robots person aware by recognizing human behaviour and by providing recognizable behaviour. Finally, probabilistic reasoning and decision making is added to enable autonomous cognitive models of human-robot interactive behaviours. As part of the course, students implement their cognitive models for a given context on a robot and investigate the requirements for social intelligence of robots.

Students learn:
to develop biologically inspired probabilistic models for human-robot interaction.
about state-of-the art technologies
about experimental methods to validate robot performance and user experience
to implement their model on the Nao robot
Topics in this course include:
Probabilistic models for perception, action and decision making
Bayes filter, Kalman filter, Particle filter
Robot proxemics and personal space models
Behaviour-based navigation
Recognising social cues:

• Gesture and facial expression recognition
• Turn-taking and eye-contact

Providing natural cues for social interaction
(Non-)verbal communication and dialog management
Monitoring attention, Joint attention and context awareness
Mental models for robots: a robot theory of mind

You must meet one of the following collections of requirements

• Collection 1
• Completed Final examination Bsc program
• Collection 2
• Completed Pre-Master

• Selected articles. (handout)
• Sebastian Thrun, Wolfram Burgard, and Dieter Fox. Probabilistic Robotics. MIT Press, 2005