How people talk when teaching a robot

Authors:
Elizabeth S. Kim;Dan Leyzberg;Katherine M. Tsui;Brian Scassellati
Affiliations:
Yale University, New Haven, CT, USA;Yale University, New Haven, CT, USA;Univ. of Massachusetts, Lowell, Lowell, MA, USA;Yale University, New Haven, CT, USA
Venue:
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Year:
2009

Citing 16
Cited 6

Wizard of Oz studies: why and how

IUI '93 Proceedings of the 1st international conference on Intelligent user interfaces
Computers are social actors

CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Virtual petz (video session): a hybrid approach to creating autonomous, lifelike dogz and catz

AGENTS '98 Proceedings of the second international conference on Autonomous agents
A social reinforcement learning agent

Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Integrated learning for interactive synthetic characters

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Recognition of Affective Communicative Intent in Robot-Directed Speech

Autonomous Robots
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
Analysis of emotion recognition using facial expressions, speech and multimodal information

Proceedings of the 6th international conference on Multimodal interfaces
Multimodal affect recognition in learning environments

Proceedings of the 13th annual ACM international conference on Multimedia
A survey of affect recognition methods: audio, visual and spontaneous expressions

Proceedings of the 9th international conference on Multimodal interfaces
Robotic vocabulary building using extension inference and implicit contrast

Artificial Intelligence
Reinforcement learning with human teachers: evidence of feedback and guidance with implications for learning performance

AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Reinforcement learning: a survey

Journal of Artificial Intelligence Research
Emotion and reinforcement: affective facial expressions facilitate robot learning

ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Learning and interacting in human-robot domains

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

No fair!!: an interaction with a cheating robot

Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
How do you play with a robotic toy animal?: a long-term study of Pleo

Proceedings of the 9th International Conference on Interaction Design and Children
2010 Special Issue: Communication and knowledge sharing in human-robot interaction and learning from demonstration

Neural Networks
A formal framework for combining natural instruction and demonstration for end-user programming

Proceedings of the 16th international conference on Intelligent user interfaces
How do you like me in this: user embodiment preferences for companion agents

IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
A bayesian network approach to investigating user-robot personality matching

AMT'12 Proceedings of the 8th international conference on Active Media Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

We examine affective vocalizations provided by human teachers to robotic learners. In unscripted one-on-one interactions, participants provided vocal input to a robotic dinosaur as the robot selected toy buildings to knock down. We find that (1) people vary their vocal input depending on the learner's performance history, (2) people do not wait until a robotic learner completes an action before they provide input and (3) people naively and spontaneously use intensely affective prosody. Our findings suggest modifications may be needed to traditional machine learning models to better fit observed human tendencies. Our observations of human behavior contradict the popular assumptions made by machine learning algorithms (in particular, reinforcement learning) that the reward function is stationary and path-independent for social learning interactions. We also propose an interaction taxonomy that describes three phases of a human-teacher's vocalizations: direction, spoken before an action is taken; guidance, spoken as the learner communicates an intended action; and feedback, spoken in response to a completed action.