the child apply skills across various contexts.
Importantly, in humans, the key element that enables the above techniques to be successful is meta-learning. Children can go from being directed in a task through leading questions and hints to internalizing that process and being able to achieve the task on their own. Thus, in robots, it is important to not only follow instructions and model the specific activity, but to learn task strategies (e.g., questions to ask, what to pay attention to, etc.), from these interactions.
2.3 ROLE OF COMMUNICATION IN SOCIAL LEARNING
2.3.1 EXPRESSION PROVIDES FEEDBACK TO GUIDE A TEACHER
To be a good instructor, one must maintain a mental model of the learner’s state (e.g., what is understood so far, what remains confusing or unknown) in order to appropriately structure the learning task with timely feedback and guidance. The learner helps the instructor by expressing their internal state via communicative acts (e.g., expressions, gestures, or vocalizations that reveal understanding, confusion, attention, etc.). Through reciprocal and tightly coupled interaction, the learner and instructor cooperate to aid both the instructor’s ability to maintain a good mental model of the learner, and the learner’s ability to leverage from instruction to build the appropriate models, representations, and associations.
With this view of learning as a tightly coupled collaboration, theories of human cooperative and collaborative activity help inform the design of robot learners. Cohen et al. analyzed task dialogs in which an expert instructed a novice assembling a physical device, and found that much of task dialog can be viewed in terms of joint intentions [72]. Their study identified key discourse functions including: organizational markers that synchronize the start of new joint actions (“now,” “next,” etc.), elaborations and clarifications for when the expert believes the apprentice does not understand, and confirmations establishing the mutual belief that a step was accomplished. Another important work is that of Bratman, in which he defines prerequisites for an activity to be considered shared and cooperative, stressing the importance of mutual responsiveness, commitment to the joint activity and commitment to mutual support [34]. Cohen et al. support these guidelines and also predict that an efficient and robust collaboration scheme in a changing environment needs an open channel of communication.
These theories argue for the importance of sharing information through communication in order to maintain a successful collaborative activity. Thus, a robot learner that people will find collaborative and cooperative, must take into account nonverbal communication, such as gestures and gaze, to facilitate the interaction and maintain an understandable transparent interface between the human and the machine.
2.3.2 ASKING QUESTIONS
In developmental psychology, the role of curiosity and inquiry is highlighted time and again as a crucial component to the learning process. Early in development this is characterized in self-learning where there is an active process of effectively asking questions of the environment. Piagetian self-regulatory reflexes (e.g., sucking, grasping, circular reactions) are crucial to early learning, helping infants/children obtain developmentally appropriate experiences for learning [207]. The work of Gopnik has additionally shown that children (and adults) are highly efficient in this process. In one study, Gopnik and colleagues demonstrated to children a “blicket machine” that made a sound when certain objects were put near it but not others. When asked to figure out how to make it go, they observed that 2, 3, and 4-year olds would efficiently explore the environment with actions (interventions) to uncover the pattern of conditional dependence between objects and the sound, inferring the causal structure of the machine [97].
Later, children become experts in actively seeking knowledge from their social environment, first becoming proficient at deciding to whom to pay attention. Movellan showed that children are highly efficient in their behavior, and in the face of deciding whether or not someone or something is reacting contingently to themselves, optimize their actions to gain the most information [178]. Thus, even pre-verbal children that cannot “ask questions” in the traditional sense of the term, are not passive observers but active learners in their world.
Educational psychology gives another view, looking at questions in a pedagogical context. Grasser and Person studied tutoring sessions in both grade school and college students, classifying a variety of question categories, under two main groups, those requiring short answers vs. long answers. They then studied the frequency and intent of various questions in real tutorial settings. They found the frequency of different types of questions was similar across two different settings, and that students primarily ask questions because of a knowledge deficit and to maintain common ground (e.g., confirming knowledge) [98]. In other research they have shown that the quality of a student’s questions and the completeness of their answers are the best predictors of final exam performance. Hence, performance was not correlated with answers students gave to confirming questions like “did you get that” [204]. Thus, a good teacher must do more than ask for knowledge confirmations to maintain a good mental model of the learner’s current knowledge.
Figure 2.4: Simon, at Georgia Tech, is one example of a robot designed with both learning and social interaction in mind. Techniques for making use of scaffolding, attention direction, transparency, and question asking are central to the development of this system.
These experiments quantifying question usage are closely related to HRI goals, and techniques integrating some of these principles into LfD will be discussed in Chapter 6.
2.4 IMPLICATIONS FOR THE DESIGN OF ROBOT LEARNERS
The human learning process serves as an inspiration in the design of social learning robots. By studying human learning we gain insights into the design of advanced learning systems. Furthermore, because learning from demonstration inherently requires interaction between the robot and the user, designing the interaction to conform to the user’s expectations leads to a more natural and effective learning process. The extent to which social elements need to be integrated into LfD often depends on the application. In some circumstances, the robot may benefit from the full range of social interactions, taking into account social cues such as gestures, gaze, direction of attention, and possibly even extending to affect. In other applications, minimal or no social understanding may be required, with the interaction instead limited to a human-computer interface. In all cases, the designers of the robot strive for the most natural, flexible, and efficient learning system for the given task. The following design elements are some that should be considered in the design of robots that learn from demonstration.
• Social interaction. Should the robot leverage the social aspect of the interaction? Would learning be aided if the robot understood the social cues of the user? Would learning be aided if the robot could exhibit social cues? Which social cues are most effective for LfD interactions? Which social cues, whether from the robot or teacher, are most informative for task learning, and which social cues are most preferred by users?
• Motivation for learning. Does the robot require intrinsic motivation for learning, or will all learning be initiated and directed by the human user?
• Transparency. To be effective, a teacher must be able to maintain an as accurate a mental model of the learner’s knowledge as possible. How can the robot externalize what it has learned and make elements of the internal model transparent to the user? What techniques for communicating the learner’s knowledge should be used to aid the learning process? Is it necessary that the communication techniques mimic the way humans communicate, or is it equally (or more) effective to leverage interfaces that are not part of natural human communication, such as screen-based devices?
• Question asking. Asking questions is a critical part of the
