Teaching robot companions: the role of scaffolding and event structuring

For robots to be more capable interaction partners they will necessarily need to adapt to the needs and requirements of their human companions. One way that the human could aid this adaptation may be by teaching the robot new ways of doing things by physically demonstrating different behaviours and tasks such that the robot learns new skills by imitating the learnt behaviours in appropriate contexts. In human–human teaching, the concept of scaffolding describes the process whereby the teacher guides the pupil to new competence levels by exploiting and extending existing competencies. In addition, the idea of event structuring can be used to describe how the teacher highlights important moments in an overall interaction episode. Scaffolding and event structuring robot skills in this way may be an attractive route in achieving robot adaptation; however, there are many ways in which a particular behaviour might be scaffolded or structured and the interaction process itself may have an effect on the robot's resulting performance. Our overall research goal is to understand how to design an appropriate human–robot interaction paradigm where the robot will be able to intervene and elicit knowledge from the human teacher in order to better understand the taught behaviour. In this article we examine some of these issues in two exploratory human–robot teaching scenarios. The first considers task structuring from the robot's viewpoint by varying the way in which a robot is taught. The experimental results illustrate that the way in which teaching is carried out, and primarily how the teaching steps are decomposed, has a critical effect on the efficiency of human teaching and the effectiveness of robot learning. The second experiment studies the problem from the human's viewpoint in an attempt to study the human teacher's spontaneous levels of event segmentation when analysing their own demonstrations of a routine home task to a robot. The results suggest the existence of some individual differences regarding the level of granularity spontaneously considered for the task segmentation and for those moments in the interaction which are viewed as most important.

[1]  Yiannis Demiris,et al.  Hierarchies of Coupled Inverse and Forward Models for Abstraction in Robot Action Planning, Recognition and Imitation , 2005 .

[2]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[3]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  I. Sigel Play, dreams and imitation in childhood. , 1953 .

[5]  Usama M. Fayyad,et al.  On the Handling of Continuous-Valued Attributes in Decision Tree Generation , 1992, Machine Learning.

[6]  Rüdiger Dillmann,et al.  Distribution and Recognition of Gestures in Human-Robot Interaction , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[7]  Chrystopher L. Nehaniv,et al.  Grounded Sensorimotor Interaction Histories in an Information Theoretic Metric Space for Robot Ontogeny , 2007, Adapt. Behav..

[8]  S. Engel Thought and Language , 1964 .

[9]  Illah R. Nourbakhsh,et al.  A survey of socially interactive robots , 2003, Robotics Auton. Syst..

[10]  Gary R. Bradski,et al.  Real time face and object tracking as a component of a perceptual user interface , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[11]  K. Dautenhahn,et al.  The correspondence problem , 2002 .

[12]  H. Evans The Study of Instinct , 1952 .

[13]  Andrea Lockerd Thomaz,et al.  Teachable robots: Understanding human teaching behavior to build more effective robot learners , 2008, Artif. Intell..

[14]  Jeffrey M. Zacks,et al.  Event structure in perception and conception. , 2001, Psychological bulletin.

[15]  J. Wertsch Vygotsky and the Social Formation of Mind , 1985 .

[16]  Ian Witten,et al.  Data Mining , 2000 .

[17]  Jeffrey M. Zacks,et al.  Perceiving, remembering, and communicating structure in events. , 2001, Journal of experimental psychology. General.

[18]  Chrystopher L. Nehaniv,et al.  Naturally Occurring Gestures in a Human-Robot Teaching Scenario , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[19]  B. Tversky,et al.  Hierarchical encoding of behavior: translating perception into action. , 2006, Journal of experimental psychology. General.

[20]  Arne Jönsson,et al.  Wizard of Oz studies: why and how , 1993, IUI '93.

[21]  William Rowan,et al.  The Study of Instinct , 1953 .

[22]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[23]  L. Vygotsky Mind in Society: The Development of Higher Psychological Processes: Harvard University Press , 1978 .

[24]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[25]  Andrea Lockerd Thomaz,et al.  Asymmetric Interpretations of Positive and Negative Human Feedback for a Social Learning Agent , 2007, RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication.

[26]  Yiannis Demiris,et al.  Abstraction in Recognition to Solve the Correspondence Problem for Robot Imitation , 2004 .

[27]  Kerstin Dautenhahn,et al.  Self-Imitation and Environmental Scaffolding for Robot Teaching , 2007 .

[28]  Aude Billard,et al.  Special Issue on Robot Learning by Observation, Demonstration, and Imitation , 2007, IEEE Trans. Syst. Man Cybern. Part B.

[29]  Chrystopher L. Nehaniv,et al.  Teaching robots by moulding behavior and scaffolding the environment , 2006, HRI '06.

[30]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[31]  Benedict du Boulay,et al.  Fallible, Distractible, Forgetful, Willful, and Irrational Learners , 2000 .

[32]  Chrystopher L. Nehaniv,et al.  6th Ieee International Conference on Robot & Human Interactive Communication Issues in Human/robot Task Structuring and Teaching , 2022 .

[33]  Chrystopher L. Nehaniv,et al.  Imitation with ALICE: learning to imitate corresponding actions across dissimilar embodiments , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[34]  Ran,et al.  The correspondence problem , 1998 .

[35]  R. Barker,et al.  Midwest and its children: the psychological ecology of an American town. , 1954 .

[36]  Michael A. Arbib,et al.  Affordances, effectivities, and assisted imitation: Caregivers and the directing of attention , 2007, Neurocomputing.