Socially guided machine learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 139-146).Social interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This thesis provides several contributions towards the understanding of this Socially Guided Machine Learning scenario. While the topic of human input to machine learning algorithms has been explored to some extent, prior works have not gone far enough to understand what people will try to communicate when teaching a machine and how algorithms and learning systems can be modified to better accommodate a human partner. Interface techniques have been based on intuition and assumptions rather than grounded in human behavior, and often techniques are not demonstrated or evaluated with everyday people. Using a computer game, Sophie's Kitchen, an experiment with human subjects provides several insights about how people approach the task of teaching a machine. In particular, people want to direct and guide an agent's exploration process, they quickly use the behavior of the agent to infer a mental model of the learning process, and they utilize positive and negative feedback in asymmetric ways.(cont.) Using a robotic platform, Leonardo, and 200 people in follow-up studies of modified versions of the Sophie's Kitchen game, four research themes are developed. The use of human guidance in a machine learning exploration can be successfully incorporated to improve learning performance. Novel learning approaches demonstrate aspects of goal-oriented learning. The transparency of the machine learner can have significant effects on the nature of the instruction received from the human teacher, which in turn positively impacts the learning process. Utilizing asymmetric interpretations of positive and negative feedback from a human partner, can result in a more efficient and robust learning experience.by Andrea Lockerd Thomaz.Ph.D

Socially Guided Machine Learning

Social interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This thesis provides several contributions towards the understanding of this Socially GuidedMachine Learning scenario. While the topic of human input to machine learning algorithms has been explored to some extent, prior works have not gone far enough to understand what people will try to communicate when teaching a machine and how algorithms and learning systems can be modified to better accommodate a human partner. Interface techniques have been based on intuition and assumptions rather than grounded in human behavior, and often techniques are not demonstrated or evaluated with everyday people. Using a computer game, Sophie’s Kitchen, an experiment with human subjects provides several insights about howpeople approach the task of teaching a machine. In particular, people want to direct and guide an agent’s exploration process, they quickly use the behavior of the agent to infer a mental model of the learning process, and they utilize positive and negative feedback in asymmetricways. Using a robotic platform, Leonardo, and 200 people in follow-up studies of modified versions of the Sophie’s Kitchen game, four research themes are developed. The use of human guidance in a machine learning exploration can be successfully incorporated to improve learning performance. Novel learning approaches demonstrate aspects of goal-oriented learning. The transparency of the machine learner can have significant effects on the nature of the instruction received from the human teacher, which in turn positively impacts the learning process. Utilizing asymmetric interpretations of positive and negative feedback from a human partner, can result in a more efficient and robust learning experience

Socially Guided Machine Learning Socially Guided Machine Learning: Designing an Algorithm to Learn from Real-Time Human

Socially Guided Machine Learning explores the ways in which machine learning can be designed to more fully take advantage of natural human interaction and tutelage. In this article we present a framework for studying the role real-time human interaction plays in training robots to perform new tasks. We have results from an initial user study using our experimental platform, Sophie’s World, to understand how people administer reward and punishment to teach a simulated robot a new task through Reinforcement Learning (RL). Based on this study, we identified three modifications to a standard RL algorithm to make it more amenable to learning from real-time human interaction: an embellished communication channel with both guidance and feedback, transparency behaviors, and responsiveness to errors. We are evaluating these modifications in a follow-up study. 1

Socially Guided Machine Learning

Social interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This thesis provides several contributions towards the understanding of this Socially GuidedMachine Learning scenario. While the topic of human input to machine learning algorithms has been explored to some extent, prior works have not gone far enough to understand what people will try to communicate when teaching a machine and how algorithms and learning systems can be modified to better accommodate a human partner. Interface techniques have been based on intuition and assumptions rather than grounded in human behavior, and often techniques are not demonstrated or evaluated with everyday people. Using a computer game, Sophie’s Kitchen, an experiment with human subjects provides several insights about howpeople approach the task of teaching a machine. In particular, people want to direct and guide an agent’s exploration process, they quickly use the behavior of the agent to infer a mental model of the learning process, and they utilize positive and negative feedback in asymmetricways. Using a robotic platform, Leonardo, and 200 people in follow-up studies of modified versions of the Sophie’s Kitchen game, four research themes are developed. The use of human guidance in a machine learning exploration can be successfully incorporated to improve learning performance. Novel learning approaches demonstrate aspects of goal-oriented learning. The transparency of the machine learner can have significant effects on the nature of the instruction received from the human teacher, which in turn positively impacts the learning process. Utilizing asymmetric interpretations of positive and negative feedback from a human partner, can result in a more efficient and robust learning experience

Effects of nonverbal communication on efficiency and robustness in human-robot teamwork

Abstract — Nonverbal communication plays an important role in coordinating teammates ’ actions for collaborative activities. In this paper, we explore the impact of non-verbal social cues and behavior on task performance by a human-robot team. We report our results from an experiment where naïve human subjects guide a robot to perform a physical task using speech and gesture. The robot communicates either implicitly through behavior or explicitly through non-verbal social cues. Both selfreport via questionnaire and behavioral analysis of video offer evidence to support our hypothesis that implicit non-verbal communication positively impacts human-robot task performance with respect to understandability of the robot, efficiency of task performance, and robustness to errors that arise from miscommunication. Whereas it is already well accepted that social cues enhance the likeability of robots and animated agents, our results offer promising evidence that they can also serve a pragmatic role in improving the effectiveness human-robot teamwork where the robot serves as a cooperative partner