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Persuasive Robotics is the study of persuasion as it applies to human-robot interaction (HRI). Persuasion can be generally defined as an attempt to change another's beliefs or behavior. The act of influencing others is fundamental to nearly every type of social interaction. Any agent desiring to seamlessly operate in a social manner will need to incorporate this type of core human behavior. As in...
This paper describes the hardware and algorithms for a realtime social touch gesture recognition system. Early experiments involve a sensate bear test-rig with full body touch sensing, sensor visualization and gesture recognition capabilities. Algorithms are based on real humans interacting with a plush bear. In developing a preliminary gesture library with thirteen symbolic gestures and eight touch...
Spatial scaffolding is a naturally occurring human teaching behavior, in which teachers use their bodies to spatially structure the learning environment to direct the attention of the learner. Robotic systems can take advantage of simple, highly reliable spatial scaffolding cues to learn from human teachers. We present an integrated robotic architecture that combines social attention and machine learning...
Human-robot interaction (HRI) is now well enough understood to allow us to build useful systems that can function outside of the laboratory. We are studying long-term interaction in natural user environments and describe the implementation of a robot designed to help individuals effect behavior change while dieting. Our robotic weight loss coach is compared to a standalone computer and a paper log...
This paper describes a robotic puppeteering system used in a theatrical production involving one robot and two human performers on stage. We draw from acting theory and human-robot interaction to develop a hybrid-control puppeteering interface which combines reactive expressive gestures and parametric behaviors with a point-of-view eye contact module. Our design addresses two core considerations:...
Robots as an embodied, multi-modal technology have great potential to be used as a new type of communication device. In this paper we outline our development of the Huggable robot as a semi-autonomous robot avatar for two specific types of remote interaction - family communication and education. Through our discussion we highlight how we have applied six important elements in our system to allow for...
We present a learning mechanism, socially guided exploration, in which a robot learns new tasks through a combination of self-exploration and social interaction. The system's motivational drives (novelty, mastery), along with social scaffolding from a human partner, bias behavior to create learning opportunities for a reinforcement learning mechanism. The system is able to learn on its own, but can...
The ability for people to interact with robots and teach them new skills will be crucial to the successful application of robots in everyday human environments. In order to design agents that learn efficiently and effectively from their instruction, it is important to understand how people, that are not experts in Machine Learning or robotics, will try to teach social robots. In prior work we have...
We present a learning mechanism, Socially Guided Exploration, in which a robot learns new tasks through a combination of self-exploration and social interaction. The system's motivational drives (novelty, mastery), along with social scaffolding from a human partner, bias behavior to create learning opportunities for a Reinforcement Learning mechanism. The system is able to learn on its own, but can...
When a human learns a new motor skill from a teacher, they learn using multiple channels: They receive high level information aurally about the skill, visual information about how another performs the skill, and at times, tactile information, from a teacher's physical guidance of the student. This research proposes a novel approach, the application of this tactile feedback through a robotic wearable...
While reinforcement learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional...
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